The Operations Catalog · A Personal Reference

01

The Anchor

The Plan

A 12-month staged build, $350/month max, emergency fund untouched.

Month 12

The Destination

Twelve months of discipline. One weekend of building. A lifetime of capability.

A staged build, twelve months long, designed to respect cash flow and protect the emergency fund. The plan validates the use case on existing hardware before the new PC arrives — and refuses to spend money the validation hasn't earned.

The Five Operating Principles

$350/mo maximum financial commitment — non-negotiable
Emergency fund stays untouched (your 3-month buffer is sacred)
Maximum 2 hard credit pulls over 12 months (Microcenter + Best Buy)
No deferred-interest term we can't pay off in cash flow
Validate use case on Optiplex first — plan serves you, not vice versa

Month-by-Month

M01

Month 1

Validation

Set up Ollama on Optiplex. Open HYSA. Auto-transfer $350/mo. Start using local LLMs for real tasks.

Save $350

First model running

M02

Month 2

Validation

Daily local LLM use. Begin PC Build Journal. Track what works vs cloud Claude.

Save $350

5+ documented use cases

M03

Month 3

Checkpoint

Honest review: am I using it enough? Look at GPU market. Total saved: $1,050.

Save $350

Use case validated or pivot

M04

Month 4

Pre-buy prep

Pull credit report. Score should be 710-720. Total saved: $1,400.

Save $350

Foundation cash ready

M05

Month 5

Foundation buy

Microcenter trip: 9950X + ProArt X870E + 64GB DDR5. $1,000 cash + ~$280 on Microcenter card.

Spend $1280

Core platform owned

M06

Month 6

Recovery

Pay Microcenter card. Save $300.

Spend $50 Save $300

Building cash back up

M07

Month 7

Recovery

Pay Microcenter card. Save $300.

Spend $50 Save $300

M08

Month 8

Recovery

Knock out Microcenter card early. Save $250.

Spend $100 Save $250

Microcenter PAID OFF ✅

M09

Month 9

GPU day

Assess GPU market. Buy RTX 5080 ($999) on Best Buy card for 10% back. $200 cash + $800 financed.

Spend $200

GPU acquired

M10

Month 10

Assembly prep

PSU (Corsair HX1200i) + Case (Define 7). Use Affirm Pay-in-4. $70 BB card + $190 Affirm.

Spend $260

M11

Month 11

Final parts

SSDs + cooler + UPS + fans. Affirm Pay-in-4. $70 BB + $175 Affirm.

Spend $245

All parts on hand

M12

Month 12

BUILD WEEKEND

Block weekend. Build the rig. Install OS. Migrate workflows from Optiplex.

Spend $70 Save $100

🔧 SYSTEM ONLINE

M13

Month 13

Cleanup

Pay down remaining Best Buy balance.

Spend $70 Save $280

M14

Month 14

Cleanup

Best Buy nearly paid off.

Spend $70 Save $280

M15

Month 15

Done

Final BB payment. Debt-free with $4K rig.

Spend $70 Save $280

🎉 DEBT FREE

Risk Register & Mitigations

RISK

Missed payment on 0% promo

Set autopay for minimum on every card the day you open it. Pay above minimum manually.

RISK

Life happens (car, dental, family)

Months 1-4 have zero commitments — pause savings safely. Months 5+ have low minimums to fall back to.

RISK

Deferred interest cliff

Pay off store cards 1-2 months BEFORE term ends, not on the deadline.

RISK

GPU prices spike

Plan allows 1-2 month delay at Month 9 without breaking anything.

RISK

Use case doesn't justify build

Month 3 checkpoint is real — pivot to smaller build or scrap entirely.

02

The Software

Local LLMs

Models, tasks, and validation protocols for clinical pharmacy and beyond.

The headline rule of local language models: capability is gated by VRAM. Everything else follows from this. Choose models that fit your hardware, validate them ruthlessly before trust, and let the GPU upgrade path open richer options later.

Model Catalog

VRAM shown as Q4 / Q8 GB. Stars rate clinical accuracy, coding, reasoning, and speed (5 = best).

Model	Size	VRAM (Q4/Q8)	Clinical	Coding	Reasoning	Speed	Notes
Qwen3 14B general	14B	9 / 16	●●●●○	●●●●○	●●●●○	●●●●●	Excellent generalist. Q8 fits on 16GB cards. Strong instruction following.
Qwen3 32B flagship	32B	20 / 36	●●●●●	●●●●●	●●●●●	●●●○○	Near-Sonnet quality for many tasks. Needs 24GB+ VRAM at Q4. Your target post-upgrade.
Qwen2.5-Coder 32B coding	32B	20 / 36	●●○○○	●●●●●	●●●●○	●●●○○	The coding workhorse. Matches GPT-4 on many code benchmarks. Worth waiting for 24GB+ VRAM.
DeepSeek R1 distilled 14B reasoning	14B	9 / 16	●●●●○	●●●●○	●●●●●	●●●●○	Visible chain-of-thought. Great for DDx exercises — you can audit reasoning.
DeepSeek R1 32B reasoning	32B	20 / 36	●●●●●	●●●●○	●●●●●	●●●○○	The reasoning king at 32B. Visible thinking, strong DDx capabilities.
Devstral 24B coding	24B	14 / 26	●●○○○	●●●●●	●●●●○	●●●●○	Agentic coding specialist. Edits files, runs commands. Pairs with VS Code.
Gemma 3 27B general	27B	17 / 30	●●●●○	●●●○○	●●●●○	●●●○○	Strong biomedical knowledge from Google. Tight fit on 16GB but doable at Q3.
Gemma 4 E4B small-fast	4B-effective	3 / 5	●●●○○	●●●○○	●●●○○	●●●●●	Surprisingly capable for size. Got vancomycin AUC right in your prior LM Studio testing.
Phi-4 14B reasoning	14B	9 / 16	●●●○○	●●●●○	●●●●●	●●●●○	Microsoft's analytical model. Excellent for structured reasoning, math, logic.
MedGemma 4B medical	4B	3 / 5	●●●●○	●○○○○	●●●○○	●●●●●	Medical-specific. Multimodal (images). Based on Gemma 3, so may miss recent guidelines.
Llama 3.3 70B flagship	70B	42 / 75	●●●●●	●●●●○	●●●●○	●●○○○	Requires 48GB+ VRAM (dual GPU or 5090). The headline 'datacenter at home' model.

VRAM Fit Calculator

Will this model run on this GPU? Quick reality check.

Your GPU VRAM

Model Size

Quantization

Context Length

Adjust controls to see if it fits.

Task Library — Clinical Workflows

Principle

Local LLMs are thinking partners, never truth sources. Every clinical output gets verified against Lexicomp, UpToDate, Sanford Guide, or current guidelines. Build verification into the workflow as a non-negotiable step.

DeepSeek R1 14B/32B

Differential diagnosis exercise

Framework: Visible reasoning lets you audit chain. Use as thinking partner, never as decision tool.

Example: 65yo M, new DOE + LE edema. Walk through DDx considering HF, PE, anemia, renal, hepatic. Audit your own reasoning against model's.

Use your own tool — LLM helps build it

Vancomycin AUC TDM

Framework: Build deterministic calculator. LLM helps explain results to colleagues.

Example: Bayesian first-order PK estimation. ALWAYS deterministic math, never LLM math.

Qwen3 14B + RAG (DailyMed/Lexicomp)

Drug interaction lookup

Framework: RAG over authoritative source. LLM synthesizes, source provides facts.

Example: Pull DailyMed JSON for two drugs, feed to model, ask for synthesis with citations.

Qwen3 14B Q8 or Gemma 3 27B

Patient education draft

Framework: LLM drafts at target reading level, YOU verify and adjust.

Example: 'Explain warfarin INR monitoring at 6th grade reading level' → review, edit, give to patient.

Phi-4 14B + paper PDFs

Literature synthesis

Framework: Feed full paper, request structured extraction. Verify quotes against source.

Example: 10 RCTs on SGLT2i in HFrEF → extract: trial, n, intervention, primary endpoint, NNT, key adverse events.

Qwen3 14B (local = privacy)

Clinical note de-identification

Framework: Local-only processing. Never send PHI to cloud.

Example: Replace names/MRNs/dates with placeholders for case-based teaching.

Gemma 3 27B or Qwen3 32B

Pharmacology teaching prep

Framework: Generate Socratic questions, then verify mechanism explanations against Goodman & Gilman.

Example: 'Generate 5 progressively harder questions on beta-blocker mechanism' → use for student session.

Qwen3 14B + local guideline PDFs

Antibiotic stewardship review

Framework: RAG over your institution's antibiogram + IDSA guidelines.

Example: 'For CAP in this patient with these allergies and renal function, what does our guideline recommend?'

Task Library — Coding

Qwen2.5-Coder 32B or Devstral 24B

Project scaffolding

Framework: Describe project, get structure + boilerplate. Always review before running.

Example: FastAPI + SQLite vancomycin TDM webapp → full skeleton with routes, models, tests.

Qwen2.5-Coder 32B

Refactoring

Framework: Paste function, request refactor with explanation. Run tests after.

Example: Turn this 200-line procedural script into clean class-based design.

DeepSeek R1 14B (visible reasoning)

Bug hunting

Framework: Paste error + relevant code. R1's thinking shows you the diagnostic path.

Example: 'Why does my Bayesian PK calculation give negative volumes?' → R1 walks the math.

Devstral 24B or Qwen2.5-Coder 32B

Test generation

Framework: Paste function, request pytest test cases including edge cases.

Example: Generate tests for vancomycin loading dose calculator including renal extremes.

Phi-4 14B

SQL/data wrangling

Framework: Describe schema + desired output. Verify on sample data before production.

Example: Pivot pharmacy dispensing log into monthly DDDs per ward.

Qwen3 14B

Documentation writing

Framework: Function → docstring. Codebase → README. Always edit for accuracy.

Example: Generate API documentation for your TDM tool with examples.

Task Library — Research

Qwen3 14B

PubMed query construction

Framework: Describe research question → get MeSH terms + search string.

Example: 'Vancomycin AUC vs trough monitoring outcomes in MRSA bacteremia' → complete PubMed query.

Phi-4 14B or Qwen3 32B

Paper summary + critique

Framework: PDF → structured summary (background, methods, results, limitations, take-home).

Example: Summarize NEJM HF trial with focus on subgroup analyses.

DeepSeek R1 14B/32B

Methodology critique

Framework: Visible reasoning catches flaws step-by-step.

Example: Critique this RCT's randomization, blinding, and analysis plan.

Qwen3 14B + local vault

Note synthesis (Obsidian-style)

Framework: RAG over your notes. Ask questions across your accumulated knowledge.

Example: 'Show me everything I've written about vancomycin PK and synthesize key principles.'

Task Library — Automation

Devstral 24B

Web scraping helper

Framework: Describe target site + data needed → working scraper. Always respect robots.txt.

Example: Daily FDA drug shortage updates → JSON for tracking.

Qwen3 14B running on Optiplex

Email triage

Framework: LLM tags incoming emails by category, urgency. Local = privacy.

Example: CME reminders, work emails, personal, newsletters → auto-foldered.

Phi-4 14B

Document classification

Framework: Drop PDFs in folder → auto-sorted by content.

Example: Downloaded paper goes to /research/, drug monograph to /clinical/, etc.

Qwen3 14B + RSS feeds

Daily briefing generator

Framework: Cron job pulls feeds, LLM summarizes, emails you at 6 AM.

Example: Top 5 clinical pharmacy headlines + 3 tech headlines, 200 words total.

Install Hub — One-Copy Setup Commands

Every command below is tested on macOS (Apple Silicon + Intel) and Ubuntu 22.04+. Click copy on any block.

Ollama — Run any model in one line

The fastest path to local LLMs. Handles downloads, GGUF conversion, and an OpenAI-compatible API on port 11434.

# Install (macOS + Linux)
curl -fsSL https://ollama.ai/install.sh | sh

# Pull models
ollama pull qwen3:14b
ollama pull deepseek-r1:14b
ollama pull devstral

# Run interactively
ollama run qwen3:14b

# List downloaded models
ollama list

# API test
curl http://localhost:11434/v1/models

LM Studio — GUI + headless API server

Best for exploring models visually. Exposes an OpenAI-compatible server on port 1234.

# macOS (Homebrew)
brew install --cask lm-studio

# Or download: lmstudio.ai

# Headless CLI server (no GUI needed)
lms load qwen3-14b-instruct \
  --context-length 32768 \
  --gpu max

lms status   # confirm server on :1234

# Test
curl http://localhost:1234/v1/models

Open WebUI — ChatGPT UI for Ollama

Full-featured chat interface with RAG, image gen, plugins, and user management. Connects to Ollama automatically.

docker run -d \
  --restart unless-stopped \
  -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  -v open-webui:/app/backend/data \
  --name open-webui \
  ghcr.io/open-webui/open-webui:main

# Visit http://localhost:3000
# First launch creates admin account

llama.cpp — Low-level, maximum control

Build from source for Metal (macOS) or CUDA acceleration. Foundation under Ollama.

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp

# macOS Metal (Apple Silicon or AMD)
cmake -B build -DGGML_METAL=ON
cmake --build build --config Release -j$(nproc)

# Run a model
./build/bin/llama-cli \
  -m models/qwen3-14b-q8.gguf \
  -n 512 \
  -p "Explain vancomycin AUC monitoring"

vLLM — Production inference (CUDA)

OpenAI-compatible API server with PagedAttention for high throughput. Requires Python + NVIDIA GPU.

pip install vllm

python -m vllm.entrypoints.openai.api_server \
  --model Qwen/Qwen2.5-14B-Instruct \
  --dtype float16 \
  --gpu-memory-utilization 0.85 \
  --port 8000

# Test
curl http://localhost:8000/v1/models

AnythingLLM — All-in-one local RAG

Connect PDFs, websites, and databases to your local models. Docker server or native desktop app.

export STORAGE=$HOME/anythingllm
mkdir -p $STORAGE

docker run -d \
  -p 3001:3001 \
  -v $STORAGE:/app/server/storage \
  --name anythingllm \
  mintplexlabs/anythingllm

# Visit http://localhost:3001

Model Pull Reference — Matched to Your VRAM

# Current Optiplex (integrated / low VRAM)
ollama pull phi4:mini          # 3.8B — fast, capable
ollama pull qwen3:4b           # Excellent for size

# After 16GB VRAM upgrade
ollama pull qwen3:14b          # Primary daily driver
ollama pull deepseek-r1:14b    # Reasoning + visible CoT
ollama pull gemma3:12b         # Strong biomedical knowledge

# RTX 5080 target (16GB VRAM)
ollama pull qwen3:32b          # Near-Sonnet quality
ollama pull devstral           # Best open coding agent
ollama pull qwen2.5-coder:32b  # Code specialist

# Run with system prompt
ollama run qwen3:14b --system "You are a clinical pharmacy AI. Be precise, cite evidence levels."

Watch: Getting Started with Local LLMs

15:22

Ollama in 15 Minutes — Run Models Locally for Free

Tutorial Setup Guide

The Generalized Vancomycin Test

Every clinical model gets tested on facts where you know the answer cold.

Protocol

1. Pick 5 questions where YOU know the right answer (vanco AUC, warfarin INR ranges, etc.)
2. Ask the model each. Record exact response.
3. Grade: pass/fail/dangerous (confident wrong is worst)
4. If 4/5 or 5/5 pass → safe for THINKING PARTNER role with verification
5. If 3/5 → useful for non-clinical tasks only
6. If <3/5 → uninstall, save space
7. Re-test quarterly (models update, drift happens)

Log a Validation Test

Record each test you run. Synced across devices, exportable.

Model

Question Tested

Verdict

Notes

03

The Encyclopedia

Hardware

Component deep dives, spec literacy, and the principles behind choosing well.

RTX graphics card — selective focus editorial photography

The Most Important Component

VRAM Is Everything

For LLMs, the GPU isn't about speed — it's about how much model fits.

Every component answers a different question. The GPU answers how much can I run. The motherboard answers what can I add later. The PSU answers will this be safe in five years. Get the literacy right once and the choices become obvious.

GPU — The Most Important Component for LLM+Gaming

Why it matters: For LLMs: VRAM is everything (model fit). For gaming: raw compute + ray tracing. The 5080 sits at the sweet spot for both — for now.

Spec Literacy

VRAM

Single most important spec for LLMs. Model size × 0.6 ≈ minimum VRAM at Q4. 32B model = ~20GB needed.

memoryBandwidth

GDDR7 (5080/5090) = 960-1792 GB/s. Higher = faster token generation. Matters more than core count for inference.

TDP

Thermal Design Power. 5080 = 360W, 5090 = 575W. Determines PSU needs + heat in your room.

rasterPerf

Traditional gaming fps. 5080 ≈ 4090 raster in many titles.

rayTracing

Blackwell (5000-series) is significantly better than Ada (4000-series) at RT.

DLSS

DLSS 4 (5000-series only) Multi-Frame Generation. Real frame rate doubler/tripler in supported games.

powerConnectors

12V-2x6 (new standard, 600W single cable). 5080+ exclusively use this. PSU must support.

GPU fan array — the cooling system that keeps inference running

Graphics card on workbench — the single most important component

AIB Decoder Ring

5080 Founders Edition (FE)

NVIDIA's own. Smallest, cleanest, quietest under load. Usually MSRP.

AIB variants (Asus, MSI, Zotac, Gigabyte, PNY)

Add factory OC, beefier coolers, RGB. $50-300 more.

ZOTAC Solid Core OC

Budget AIB. Often at MSRP. Decent cooler, no RGB drama.

MSI Shadow / Inspire

Mid-tier AIB. Good cooler, reasonable price premium.

ASUS ROG / Strix

Premium AIB. Best cooling, premium price ($150-300 over MSRP).

Gigabyte AORUS / Master

High-end AIB. Watch for QC complaints on some runs.

Current Landscape

Part	Price	Notes
RTX 5050 entry vram: 8 · llmCeiling: 8B Q4 only · gaming: 1080p · priceMSRP: 249		Skip for your use case — VRAM too low for serious LLM.
RTX 5060 Ti 16GB budget vram: 16 · llmCeiling: 14B Q4, 22B Q3 · gaming: 1080p/1440p · priceMSRP: 429		Surprise hero. Same VRAM as 5080 at half the price. Weaker compute but FITS the models.
RTX 5070 midrange vram: 12 · llmCeiling: 12B Q4 · gaming: 1440p · priceMSRP: 549		12GB is awkward for LLMs. Skip in favor of 5060 Ti 16GB if budget.
RTX 5070 Ti midrange-high vram: 16 · llmCeiling: 14B Q8 / 22B Q4 · gaming: 1440p high · priceMSRP: 749		Strong middle ground. Often $50-100 cheaper than 5080 with 80% performance.
RTX 5080 high-end vram: 16 · llmCeiling: 14B Q8 / 24B Q4 / 27B Q3 · gaming: 1440p ultra / 4K high · priceMSRP: 999		Your locked target. Best new-card balance for hybrid use.
RTX 5080 Super (rumored) halo-mid vram: 24 · llmCeiling: 32B Q4 comfortably · gaming: 1440p/4K · priceMSRP: 1199		Rumored late 2026. Would change everything — wait if you can.
RTX 5090 halo vram: 32 · llmCeiling: 32B Q8 / 70B Q3 · gaming: 4K ultra everything · priceMSRP: 1999		The dream. Street price $2900-3900 currently. Wait for normalization.
RTX 4090 (used) previous-halo vram: 24 · llmCeiling: 32B Q4 with context · gaming: 4K ultra · priceMSRP: $1500-2000 used		If you find one ~$1500, strongly consider. 24GB is the magic number.
RTX 3090 (used) older-halo vram: 24 · llmCeiling: 32B Q4 / 70B Q2 · gaming: 1440p/4K capable · priceMSRP: $700-900 used		The local LLM community's darling. 24GB VRAM for cheap. Gaming weaker but adequate.
RX 7900 XTX (AMD) high-end-AMD vram: 24 · llmCeiling: 32B Q4 with ROCm · gaming: 4K capable · priceMSRP: 999		AMD path. ROCm support improving but Linux-mostly. Gaming great, LLM workflow rockier.

Red Flags When Buying Used

Ex-mining cards (look for: dust caked on backplate, missing original cooler, 'tested for 8 hrs' rather than 'lightly used')
Cards without original box/receipt if buying used at significant savings
AIBs from no-name brands you've never heard of
Anything advertised as 'mining BIOS flashed' or 'undervolt profile included'
Sellers who won't let you stress test before purchase (in-person buys)

Watch: RTX 5080 Build Walkthroughs

Best RTX 5080 Gaming PC — $2,800 Build

Build Guide 9800X3D + 5080

4K Build: 9800X3D + RTX 5080 with Benchmarks

Benchmarks 4K Gaming

CPU — Matters Less for LLMs, More for Gaming

Why it matters: Modern LLM inference is GPU-bound. CPU only matters for: (1) gaming 1% lows, (2) CPU offload when models exceed VRAM, (3) general system responsiveness.

Spec Literacy

cores

More cores ≠ better gaming. Diminishing returns past 8 P-cores. Productivity benefits from 16+.

clocks

Boost clock is the marketing number. All-core sustained matters more for sustained workloads.

L3cache

Critical for gaming. AMD's X3D chips win on cache stacking. Ryzen 7800X3D / 9800X3D = gaming king.

TDP

9950X = 170W TDP, 9700X = 105W. AIO recommended at 105W+, mandatory at 170W.

AVX512

AMD Zen 5 has native AVX-512. Boosts CPU-only LLM inference ~50%. Intel disabled it on consumer chips.

iGPU

9700X/9950X have basic iGPU (Radeon 2 CUs). Useful for troubleshooting GPU issues. X3D chips have no iGPU.

Current Landscape

Part	Price	Notes
Ryzen 7 9700X cores: 8P/16T · clock: 4.7/5.5 GHz · useCase: Best budget gaming + light productivity. No AIO required. · note: Sweet spot if budget tight.	$280
Ryzen 7 9800X3D cores: 8P/16T · clock: 4.7/5.2 GHz · useCase: Gaming king. Best 1% lows in every modern game. · note: If gaming is #1 priority, this is the answer.	$480
Ryzen 9 9900X cores: 12P/24T · clock: 4.4/5.6 GHz · useCase: More cores than 9700X but no X3D cache. Productivity middle ground. · note: Often skipped — 9700X or 9950X are usually better picks.	$430
Ryzen 9 9950X cores: 16P/32T · clock: 4.3/5.7 GHz · useCase: Productivity beast. CPU LLM offload, video, compiling. Gaming = 95% of 9800X3D. · note: Your locked choice. Best balance for your polymath workloads.	$489
Ryzen 9 9950X3D cores: 16P/32T · clock: 4.3/5.7 GHz · useCase: Gaming AND productivity king. Only chip without compromise. · note: If you can stretch, this is the no-compromise pick.	$659
Intel Core Ultra 7 265K cores: 8P+12E/24T · clock: 3.9/5.5 GHz · useCase: Intel's current mid. Underwhelming vs AMD this gen. · note: Skip — AMD wins this generation across price points.	$379
Intel Core Ultra 9 285K cores: 8P+16E/24T · clock: 3.7/5.7 GHz · useCase: Intel flagship. Slower than 9950X in most tests. · note: Skip unless Intel ecosystem reasons.	$589

Recommendation

Ryzen 9 9950X for the locked build (your polymath use case). Ryzen 7 9800X3D if you pivot to gaming-first. Skip Intel this generation.

RAM — Get Enough, Don't Overpay for Speed

Why it matters: Need >2× model size in system RAM for comfortable LLM operation. For LLMs that fit on GPU, RAM matters less. For gaming, speed barely matters above DDR5-6000.

Spec Literacy

capacity

64GB is the new sweet spot for power users. 32GB is gaming-only minimum. 128GB+ only for VM heavy workloads or huge model CPU offload.

speed

DDR5-6000 CL30 is the AM5 sweet spot. AMD's IF clock locks 1:1 here. Faster (DDR5-7200+) gives minimal gains, often de-syncs.

timings

CL30 at 6000 = lower latency than CL36. Worth the small premium. Most modern kits achieve this.

EXPO_vs_XMP

EXPO = AMD's overclock profile. XMP = Intel's. Buy EXPO-labeled kits for Ryzen. They often work on Intel too via XMP fallback.

dimms

ALWAYS buy as a kit (2×32GB for 64GB). Mixing kits causes instability. AM5 strongly prefers 2 DIMMs over 4.

rank

Single-rank vs dual-rank. At 64GB (2×32GB), they're dual-rank — slightly better performance, slightly trickier to OC.

Current Landscape

Part	Price	Notes
G.Skill Trident Z5 Neo 64GB DDR5-6000 CL30	$430	AMD EXPO native. The community-favorite kit. Your locked choice.
Corsair Vengeance 64GB DDR5-6000 CL30	$410	Solid alternative. EXPO certified. Lower-profile heatsinks (cooler clearance friendlier).
Crucial Pro 64GB DDR5-5600	$340	Budget pick. 5600 is fine, just leaves perf on table. CL40+ tends to feel sluggish.
G.Skill Trident Z5 Neo 96GB DDR5-6400	$720	If you want headroom for CPU LLM offload of 70B models. Overkill for most.
Kingston Fury Beast 128GB (4×32GB) DDR5-5600	$580	4-DIMM kits are tough on AM5. Often only runs at 4800. Avoid if possible.

Future-Proofing

Buy 2×32GB now. Mobo has 4 slots. If 128GB ever needed (CPU offload of 70B+), add another 64GB kit later — though mixing kits at full speed is dicey on AM5. Better path: sell first kit, buy 2×64GB if available by then.

Storage — Speed Matters Less Than You Think

Why it matters: For games: PCIe 4.0 NVMe is fine, PCIe 5.0 wasted money. For LLM model loading: faster = better but you load once per session. For OS: any NVMe is plenty.

Spec Literacy

interfaces

PCIe 4.0 NVMe = 7,000 MB/s sequential. PCIe 5.0 = 14,000 MB/s. Real-world gaming difference: <2 seconds load time.

DRAM_cache

DRAM-equipped drives (990 Pro, SN850X) handle sustained writes much better than DRAM-less. Worth the premium for any drive you'll write to heavily.

TBW

Terabytes Written endurance rating. 2TB consumer drives = 1200-2400 TBW. You will not hit this in 10 years of normal use.

formFactor

M.2 2280 is standard. All consumer NVMe is this size. PS5 compatibility means PCIe 4.0 + heatsink.

heatsink

Required on PCIe 5.0 drives. PCIe 4.0 usually fine with mobo's built-in heat spreader. Excessive heatsinks block GPU airflow.

controllers

Phison E26 (PCIe 5.0), Phison E18 / Samsung Elpis (PCIe 4.0 top tier). Avoid no-name controllers.

Current Landscape

Part	Price	Notes
WD Black SN850X 2TB interface: PCIe 4.0	$250	Reliable, fast, no thermal issues. Your OS+games drive.
Samsung 990 Pro 2TB interface: PCIe 4.0	$280	Slightly faster than SN850X. Samsung Magician software ecosystem. Your models drive.
Crucial T700 2TB interface: PCIe 5.0	$380	Runs hot. Need beefy heatsink. Gaming benefit: marginal.
Samsung 9100 Pro 2TB interface: PCIe 5.0	$450	Fastest consumer drive. Overkill for your use case.
Crucial P3 Plus 4TB interface: PCIe 4.0 (DRAM-less)	$280	Budget bulk storage. Slow sustained writes. Good for cold model storage.
WD Black SN770 2TB interface: PCIe 4.0 (DRAM-less)	$180	Cheap. OK for boot drive. Skip for heavy write workloads.

Strategy

Two-drive setup: 2TB for OS+games (SN850X), 2TB dedicated for AI models (990 Pro). Add 4TB+ HDD later for cold storage / Plex library.

PSU — Buy Once, Cry Once

Why it matters: Cheap PSUs kill components. Good PSUs last 10 years across multiple builds. Size for FUTURE GPU upgrade, not just today's parts.

Spec Literacy

wattage

Sum component peak draw × 1.5 buffer. Single-5080 build = 850W minimum, 1000W comfortable. Dual GPU or 5090-upgrade-path = 1200W+.

efficiency

80+ Bronze < Silver < Gold < Platinum < Titanium. Gold is the sweet spot. Platinum worth it if 24/7 use (your case).

modularity

Fully modular = remove every cable. Cleaner builds, easier installation. Worth $30-50 premium.

ATX_3_1

Latest standard. Native 12V-2x6 GPU connector (600W single cable). Required for 5080+. Older PSUs need adapters (avoid).

warranty

Top brands: 10-12 years. Bottom brands: 3-5 years. Long warranty = manufacturer confidence.

capacitors

Japanese caps > Chinese caps. Reviewers (Aris/Cybenetics, JonnyGuru) tell you which.

OCP_OPP_OVP

Over-Current/Power/Voltage Protection. Modern good PSUs have all. Cheap PSUs disable some to hit price points.

Current Landscape

Part	Price	Notes
Corsair HX1200i premium wattage: 1200 · efficiency: Platinum/ATX 3.1	$320	Your locked choice. 12-yr warranty. Quiet. iCUE software for monitoring.
Corsair RM1000x great wattage: 1000 · efficiency: Gold/ATX 3.1	$200	Excellent value. 10-yr warranty. If single-GPU lifetime, this is enough.
Seasonic PRIME TX-1300 ultra wattage: 1300 · efficiency: Titanium/ATX 3.1	$440	The gold standard. 12-yr warranty. Premium price.
Seasonic Focus GX-1000 great wattage: 1000 · efficiency: Gold/ATX 3.1	$190	Seasonic quality at midrange price. 10-yr warranty.
EVGA Supernova G7 1000W great wattage: 1000 · efficiency: Gold/ATX 3.1	$180	EVGA quality (made by Super Flower). 10-yr warranty.
Thermaltake / generic 1000W avoid wattage: 1000 · efficiency: Gold	$90	DO NOT. Cheap caps, weak protection, voids GPU warranty when they fail.

Rule

Brands worth buying: Corsair (HX/RM/SF), Seasonic (PRIME/Focus), EVGA (G/P series, Supernova line), be quiet! (Dark Power, Straight Power), Super Flower (Leadex). Avoid: anything you've never heard of, anything under $80 at 1000W.

Motherboard — The Platform Decision

Motherboard component detail — the platform that defines your upgrade path

Every trace, every socket — a five-year commitment.

Why it matters: Hardest part to upgrade later. Locks in CPU socket, RAM standard, PCIe gen, USB version. Buy for 5 years.

Spec Literacy

socket

AM5 (AMD Ryzen 7000/8000/9000) = current. LGA1851 (Intel) = current. AM5 supports through 2027 minimum (vs Intel changing every 1-2 gens).

chipset

B650 (mid, fine for most), B650E (mid+, PCIe 5.0 GPU slot), X670 (high, dual chipset), X670E (high, all PCIe 5.0), X870 (mid-new), X870E (high-new, full PCIe 5.0, USB4).

VRMs

Voltage Regulator Modules. 16+ phase good for 9950X. Weak VRMs throttle CPU under sustained load.

PCIe_lanes

x16 slot 1 + how many x8/x4 secondary. For dual GPU: need x8/x8 split. ProArt X870E does this. Cheap boards don't.

M2_slots

Want 3+ M.2 NVMe slots minimum. Future expansion. PCIe 5.0 slot routes from CPU (fastest), others from chipset.

USB4

40 Gbps. Useful for eGPU (LLM cluster expansion), fast external storage. X870E boards usually have it.

wifi

WiFi 7 on X870E. WiFi 6E on cheaper boards. Both fine.

BIOS_flashback

Lets you update BIOS without CPU installed. Critical when buying new CPU + old-stock mobo combo.

Current Landscape

Part	Price	Notes
ASUS ProArt X870E-Creator WiFi creator chipset: X870E	$510	Your locked choice. Dual USB4, dual 5G LAN, dual PCIe 5.0 x16 (x8/x8 split). Creator board = workstation tier without workstation price.
MSI MEG X870E Godlike halo chipset: X870E	$1099	Halo board. Most features. Massive price premium for limited real benefit.
ASUS ROG Strix X870E-E gaming-premium chipset: X870E	$540	Gaming-focused premium. Excellent VRMs. RGB heavy.
MSI MAG X870 Tomahawk WiFi mid chipset: X870	$330	Best value X870. Single PCIe 5.0 slot only — no dual GPU split.
Gigabyte B850 Aorus Elite WiFi budget chipset: B850	$220	Solid mid-budget. Good VRMs. No dual GPU.
ASRock B650 Pro RS minimum chipset: B650	$150	Cheapest viable. 9950X is at VRM limits — fine for stock, no OC headroom.

Decision

Pay for the platform if you plan upgrades. ProArt X870E preserves dual-GPU + USB4 + 5-year platform life. Stepping down to B650 saves $300 but kills the upgrade path.

Cooling — Adequate is Better Than Maximum

Why it matters: 9950X needs serious cooling under sustained load. LLM training spikes CPU. Gaming spikes GPU. Both spike together = thermal limits matter.

Spec Literacy

air_vs_AIO

Top-tier air (Noctua NH-D15, Phanteks T30) ≈ 240mm AIO. 360mm AIO clearly better for 170W chips like 9950X. Air = simpler/quieter at idle. AIO = better peak.

AIO_size

240mm = 7700X tier. 280mm = 9700X. 360mm = 9950X comfortable. 420mm = unnecessary unless heavy OC.

fan_quality

Cooler is only as good as its fans. Noctua/Arctic/Phanteks tier > stock fans on most AIOs.

pump_quality

Asetek-based pumps (most AIOs) > random new entrants. Arctic LF III uses their own pump — proven reliable.

radiator_position

Top intake or top exhaust both fine. Front intake = best CPU temp, worst GPU temp. Side mount = case-specific.

airflow_strategy

Positive pressure (more intake than exhaust) = less dust. Filter all intakes. Replace filters quarterly.

Current Landscape

Part	Price	Notes
Arctic Liquid Freezer III 360 A-RGB value-king type: AIO 360mm	$130	Best value AIO. Beats $200 competitors in tests. Your locked choice.
Noctua NH-D15 G2 premium-air type: Air	$150	Best air cooler made. Massive — check clearance with tall RAM. Quieter than any AIO.
Corsair iCUE H150i Elite LCD XT premium-gimmick type: AIO 360mm	$280	LCD screen on pump = gimmick. Pay for the looks.
Phanteks T30 + Glacier One 360 premium type: Custom-grade AIO	$250	T30 fans are legendary. Diminishing returns vs Arctic at 2× price.
DeepCool LT720 value type: AIO 360mm	$130	Good Arctic alternative. Slightly louder. Same performance tier.
Thermalright Phantom Spirit 120 SE budget type: Air	$40	Insane value air cooler. Beats $80 coolers. Adequate for 9700X, marginal for 9950X.

Case — Airflow > Aesthetics

Why it matters: Bad cases create thermal throttling. Good cases stay cool quietly. Future-proof for dual GPU = full tower with 3-slot GPU clearance.

Spec Literacy

size

ITX (smallest) < mATX < ATX (mid-tower) < E-ATX (full tower). You want full tower for dual GPU future + airflow + cable mgmt.

airflow_design

Mesh front panels = best airflow. Glass front panels = furnace. Define 7 = mesh option. Lian Li O11 = glass (but huge mesh elsewhere).

GPU_clearance

5080 = 305mm length, 3-slot. 5090 = 330-360mm, 3.5-slot. Measure max GPU length in case specs.

radiator_support

Top: 360mm common. Front: 360mm common. Side: less common. Plan radiator placement.

drive_bays

2.5" SSD bays + 3.5" HDD bays. NVMe goes on mobo. HDD bays for future media/backup storage.

cable_routing

Behind motherboard tray + velcro straps + grommets. Full tower has more room. Define 7 wins here.

dust_filters

All intakes should have removable washable filters. Define 7 is exemplary.

Current Landscape

Part	Price	Notes
Fractal Design Define 7 (regular) premium-silent size: Mid-tower (large)	$200	Your locked choice. Silent-focused, mesh front available, exemplary build quality.
Fractal Design Define 7 XL premium-silent-xl size: Full tower	$270	Extra room for dual GPU + radiator + HDDs. Future-proof.
Lian Li O11 Dynamic EVO XL showcase size: Full tower	$230	Showcase case. Three glass panels. Excellent thermals despite glass. Noisier.
Fractal Meshify 2 balanced size: Mid-tower	$170	Define 7 with mesh front. Slightly louder, slightly cooler.
be quiet! Silent Base 802 premium-silent size: Mid-tower	$200	Define 7 competitor. Silence-focused, swappable panels (mesh/closed).
Phanteks Eclipse G500A value size: Mid-tower	$130	Best value mesh. Solid build, great airflow.
NZXT H7 Flow value-clean size: Mid-tower	$130	Clean look, good airflow, easy to build in.

04

The Configurations

Builds

Five paths to the same goal, with honest trade-offs for each.

Gaming and LLM workstation with RGB lighting

Five Configurations

Same Goal, Different Paths

From $2,360 to $7,429 — every build has a thesis and honest trade-offs.

Each build represents a coherent answer to a different question. The locked plan answers what's the disciplined path. The used 3090 answers what's the fastest path. The 5090 dream answers what's the no-compromise path. Read each pros/cons honestly — the right build for you is the one whose cons you can live with.

Compare Builds Side-by-Side

Pick two to see them next to each other.

Build A

Build B

Build Config

THE LOCKED PLAN — 12-Month Staged

What we agreed on. RTX 5080, 64GB, ProArt, staged purchase.

Total

$3,893

Monthly

350

Gaming

1440p ultra / 4K high

LLM Ceiling

14B Q8 / 24B Q4 / 27B Q3

Parts

Category	Part	Price
CPU	Ryzen 9 9950X	$489
Cooler	Arctic Liquid Freezer III 360	$130
Mobo	ASUS ProArt X870E-Creator WiFi	$510
RAM	G.Skill Trident Z5 Neo 64GB DDR5-6000 CL30	$430
GPU	RTX 5080 16GB (MSI/Zotac)	$999
Storage 1	WD Black SN850X 2TB	$250
Storage 2	Samsung 990 Pro 2TB	$280
PSU	Corsair HX1200i 1200W Platinum	$320
Case	Fractal Design Define 7	$200
Fans	Arctic P14 PWM × 3	$35
UPS	APC Back-UPS Pro 1500VA	$220
OS	Windows 11 Pro	$30

Pros

Best long-term flexibility
Stays within $350/mo
Validated use case first
Upgrade path open

Cons

12 months to complete
16GB VRAM ceiling for first year
Some 'shortage tax' on RAM/storage

Upgrade Path

Replace 5080 w/ 5090 in 2027 OR add 2nd 5080 for dual-GPU 32GB

Build Config

USED 3090 — Fast Track Budget

Used RTX 3090 24GB, full build in 4-5 months.

Total

$2,360

Monthly

400

Gaming

1440p high, 4K capable

LLM Ceiling

32B Q4 fits comfortably

Parts

Category	Part	Price
CPU	Ryzen 7 9700X	$280
Cooler	Thermalright Phantom Spirit 120 SE	$40
Mobo	Gigabyte B850 Aorus Elite WiFi	$220
RAM	G.Skill Trident Z5 Neo 64GB DDR5-5600 CL30	$380
GPU	RTX 3090 24GB (used, eBay/r/HWS)	$750
Storage	WD Black SN850X 2TB	$250
PSU	Corsair RM850x 850W Gold	$150
Case	Phanteks Eclipse G500A	$130
UPS	APC Back-UPS 1000VA	$130
OS	Windows 11 Pro	$30

Pros

32B models run TODAY
Done in 4-5 months
Lowest total cost
Strong used GPU value

Cons

Used GPU risk
3090 power-hungry (350W)
Older platform features
Gaming weaker than 5080

Upgrade Path

Replace 3090 w/ 5090 later. Or sell at minimal loss.

Build Config

5090 DREAM — No Compromise

If money truly weren't a concern. RTX 5090 + 9950X3D + 96GB.

Total

$7,429

Monthly

Out of plan

Gaming

4K ultra everything, ray tracing maxed

LLM Ceiling

32B Q8 / 70B Q3 in VRAM

Parts

Category	Part	Price
CPU	Ryzen 9 9950X3D	$659
Cooler	Noctua NH-D15 G2	$150
Mobo	ASUS ProArt X870E-Creator WiFi	$510
RAM	G.Skill Trident Z5 Neo 96GB DDR5-6400	$720
GPU	RTX 5090 32GB (street price)	$3200
Storage 1	Samsung 990 Pro 2TB	$280
Storage 2	Samsung 9100 Pro 4TB	$750
PSU	Seasonic PRIME TX-1300 Titanium	$440
Case	Fractal Define 7 XL	$270
UPS	CyberPower CP1500PFCLCD	$250
OS	Windows 11 Pro retail	$200

Pros

No compromises at all
70B local models possible
Future-proof for 3+ years

Cons

3× your budget
Doesn't match your monthly cash flow
Shortage premium ($1200 over MSRP on GPU)

Upgrade Path

Eventual dual 5090 for true datacenter-at-home

Build Config

MAC STUDIO M5 MAX — The Apple Alternative

128GB unified memory. Excellent LLM, no gaming.

Total

$4,799

Monthly

N/A

Gaming

Mac gaming is limited (Whiskey/CrossOver, no AAA)

LLM Ceiling

70B Q4 comfortably, 120B Q3 possible

Parts

Category	Part	Price
Whole unit	Apple Mac Studio M5 Max, 128GB unified, 2TB SSD	$4799

Pros

Unified memory = 128GB 'VRAM' equivalent
Silent operation
Tiny footprint
Power efficient (200W max)

Cons

Gaming essentially excluded
Can't upgrade anything ever
Apple tax on storage/memory
macOS limits some LLM tooling

Upgrade Path

None. Sealed unit.

Build Config

DUAL 5080 FUTURE STATE — Tensor Parallelism

What the locked build evolves into in year 2.

Total

$4,850

Monthly

After year 1, ~$80/mo for 12 mo

Gaming

Single 5080 used for gaming (SLI dead)

LLM Ceiling

32B Q8 split across 2 cards via vLLM

Parts

Category	Part	Price
Existing locked build	Everything from locked plan	$3850
GPU 2	Second MSI/Zotac RTX 5080 16GB (used or new at MSRP)	$800
Cables	Additional 12V-2x6 cables	$30
Fans (heat mgmt)	2× more case fans for dual GPU heat	$25
Riser cable	PCIe 5.0 riser if vertical mount needed	$145

Pros

32GB combined VRAM for LLMs
Existing build untouched
Tensor parallelism in vLLM/llama.cpp

Cons

LLM-only benefit (no gaming dual-GPU)
Heat management complex
Two cards = two failure points

Upgrade Path

Sell pair → single 5090 if desired

05

The Hunt

Market Intel

Where to buy, what to watch for, when to act.

Patience is the cheapest performance upgrade you can buy. The same RTX 5080 that costs $1,400 in February sells at $999 by May. Knowing the seasonality, the right retailer for each part, and the red flags on used goods can save more money than any single component choice.

Where to Buy New

Physical (in-store)

Microcenter

Best for: CPU+Mobo+RAM bundles, in-stock GPU drops, lowest prices on AMD

Strengths: Bundle discounts ($100-200 off CPU+mobo+RAM), MSRP GPU stock more often than online, No tax in some states, Open-box deals

Weaknesses: Must drive there, Limited locations, Stock varies by store

Pro Tip

Saturday morning before 11 AM = best stock. Get on their 'in-stock notifications' list for hot items.

Online + physical

Best Buy

Best for: GPU at MSRP, Apple products, store financing

Strengths: NVIDIA Founders Edition exclusive, 0% financing on store card, Local pickup option, Easy returns

Weaknesses: Higher prices on non-GPU parts, Limited PC component selection

Pro Tip

Sign up for My Best Buy Plus during your purchase — free standard returns extended to 60 days.

Online

Newegg

Best for: PSUs, storage, cases, niche brands

Strengths: Largest PC parts selection, Frequent combo deals, Newegg gift card stacking

Weaknesses: Third-party seller risk (verify 'Shipped & Sold by Newegg'), RMA process can be painful

Pro Tip

Filter by 'Shipped & Sold by Newegg' only. Avoid marketplace sellers for important parts.

Online

Amazon

Best for: Peripherals, cables, accessories, fast shipping

Strengths: Prime shipping, Easy returns, Subscribe-and-save for small parts

Weaknesses: Counterfeit components risk (RAM, SSDs, cables), Mixed warehouse inventory, Higher prices on GPUs/CPUs

Pro Tip

Verify 'Ships from and sold by Amazon.com'. Avoid third-party for CPUs/GPUs/storage.

Online

B&H Photo

Best for: Workstation parts, professional gear, sales tax-free in many states

Strengths: No sales tax outside NY/NJ, Excellent customer service, Real human chat

Weaknesses: Smaller selection than Newegg, Closes Saturdays for Sabbath

Pro Tip

Check B&H first if you're in a no-NY-tax state — savings can be 8-10%.

The Used Market

Used hardware often delivers 80% of the performance at 50% of the cost — but the risk profile changes completely. Vet sellers like you'd vet a patient's history: look for the red flags first.

Reddit community marketplace

r/hardwareswap

Best for: Used GPUs, CPUs, RAM at fair prices from PC builders

Strengths: Verified user profiles (heatware), Mod-enforced rules, Mostly honest sellers, PayPal G&S protection

Weaknesses: Karma/comment requirements to post, Negotiation expected, No platform escrow

Rules:

Always PayPal Goods & Services (NEVER Friends & Family for purchases)
Check user's r/hardwareswap heatware before buying
Verify timestamped photos in listing

Pro Tip

Use r/hardwareswap_meta searches to check prices. Comment 'PM sent' on listings — quick responders trust faster.

Auction + Buy It Now

eBay

Best for: Used GPUs (with buyer protection), bulk lots, rare parts

Strengths: Strong buyer protection (eBay Money Back Guarantee), Wide selection, Auction snipes can save money

Weaknesses: Mining cards common, Sellers often inflate condition, Returns can be hassled

Rules:

Only buy from 99%+ feedback sellers
Look for 'Local pickup' for high-value items
Filter 'Buy It Now' for fixed prices

Pro Tip

Sort 'Sold' listings by date to see TRUE market price. Use eBay alerts for specific models.

Local in-person

Facebook Marketplace

Best for: Local pickup, cash deals, full prebuilt PCs

Strengths: Test before buy, No shipping cost or damage, Cash = no fees

Weaknesses: Zero buyer protection, Scammers common, Meeting strangers

Rules:

ALWAYS meet in public (police station parking lots ideal)
Bring a laptop with HWInfo/GPU-Z to test on-site
Cash only, never wire/Zelle/CashApp

Pro Tip

Build a quick GPU stress test USB stick: Ubuntu live + glmark2 + nvidia-smi monitoring.

Local in-person

Craigslist

Best for: Same as FB Marketplace — local cash deals

Strengths: Less scam-prone than FB, Established for tech sales

Weaknesses: Declining usage, Same in-person risks

Pro Tip

Most active in metro areas. Search 'gaming pc' rather than specific parts for bundle deals.

Aggregator (not seller)

r/buildapcsales

Best for: Daily curated deals at major retailers

Strengths: Community-vetted deals, Price history context, Hot deals get top-voted

Weaknesses: Deals expire fast, Some posts are affiliate-driven

Pro Tip

Subscribe to RSS feed: reddit.com/r/buildapcsales/.rss → pipe to your daily briefing.

Red Flags by Component

Each component has its own failure modes. These are the signals that should make you walk away from a "great deal."

RED FLAGS

gpu

Heat damage near power connector (browning/melting on 12VHPWR)
Missing original box (often ex-mining)
Dust caked deep in fins (sustained 24/7 use indicator)
Backplate screws stripped (opened repeatedly)
Seller can't produce purchase receipt for warranty transfer
Listing says 'works great' but no benchmark numbers

RED FLAGS

cpu

Bent pins on AM5 (LGA — pins on mobo) or LGA1700 (pins on chip)
Thermal paste residue still on IHS (lazy seller, but not catastrophic)
Seller says 'overclocked daily' (degradation risk over time)
No box / no original packaging

RED FLAGS

ram

Heatsink labels peeling (often relabeled cheap RAM)
Mismatched serial numbers on a 'kit'
Speed claims don't match SPD chip (verify with CPU-Z screenshot)
Bent pins on DIMM contacts

RED FLAGS

psu

Coil whine reported by seller (unfixable, will only get worse)
Bulging capacitors visible through vent
Burning smell ever reported
Heavy use in mining rig (PSUs degrade hard under sustained 90% load)
Past 7 years old (cap aging — replace, don't buy used)

RED FLAGS

ssd

Power-on hours >5000 (check SMART data)
Used percentage / wear leveling >20%
Reported uncorrectable errors in SMART log
No SMART screenshot in listing (deal-breaker)

Seasonality — When to Buy

Month	Market Behavior
January (CES)	New product announcements. Last-gen prices may drop. Don't buy at MSRP for 30 days after CES.
February-March	Tax refund season. Demand up. Prices flat. Bad time to buy.
April	Post-tax dip. Some sales begin.
May-June	Build season starts. Memorial Day sales (last Monday of May).
July	Amazon Prime Day (mid-July). Real deals on storage, peripherals. GPU deals rare.
August	Back-to-school. Laptop focus, not desktop parts. Some monitor sales.
September	New school year. Some component dumps as students sell.
October	Pre-holiday slowdown.
November	BLACK FRIDAY WEEK. Real GPU/CPU sales. PSUs, cases, storage all discounted. Best buying month.
December	Cyber Monday + holiday sales. PSUs and storage continue. GPU stock thin.

Deal Hunting Tools

PCPartPicker price tracker (sign up, set alerts on specific parts)
CamelCamelCamel for Amazon price history
Slickdeals.net hot deals (PC components forum)
r/buildapcsales subreddit RSS feed
Discord servers: r/hardwareswap notifier bots
Microcenter in-store stock checker (3rd party tools track inventory)
Newegg Shuffle for GPU drops (lottery system, free to enter)

06

The Bigger Picture

Ecosystem

Four devices, one architecture, complementary roles.

Four Devices, One Network

Complementary, Not Redundant

Each device earns its place. Together they cover every use case from pocket to datacenter.

Each device does what it's best at. They talk to each other. The Optiplex never sleeps and never gets in your way. The MacBook goes where you go. The new PC carries the heavy compute. The iPhone connects you to all of it.

Your 4-Device Architecture

Always-on backbone

OptiPlex 7050 (Ubuntu)

Specs: i7-6700T, 32GB, 1-2TB SSD (upgrade), Intel HD 530

Why: 35W TDP = $5/month electricity for 24/7 uptime. Perfect always-on tier.

Runs:

Open WebUI (LLM frontend)
Ollama (small models)
Plex/Jellyfin
Home Assistant
Tailscale exit node
n8n automation
Reverse proxy (Caddy)
Backup service (restic)
Future: live data fetcher for encyclopedia

Mobile command center

MacBook M4 Air 24GB

Specs: M4, 24GB unified memory, macOS

Why: 24GB unified memory runs 14-30B models surprisingly well. Best mobile LLM machine you can own.

Runs:

LM Studio / Ollama with MLX
VS Code
Browser, daily driver
Claude API for orchestration

Heavy compute station

New PC (post-Month 12)

Specs: 9950X, 64GB DDR5, RTX 5080, dual-boot Win+Linux

Why: On when you're using it. Sleeps when you're not. Carries the heavy LLM workload.

Runs:

Local LLMs at GPU speed (Qwen3 14B-24B)
Gaming
Heavy compute when home
Future: dual GPU, 5090 swap

Pocket dashboard

iPhone 15

Why: Universal access to your ecosystem via Tailscale + Open WebUI mobile.

Runs:

Open WebUI via Tailscale
Home Assistant mobile app
Quick LLM queries from anywhere

Networking & Access

Service	Purpose	Complexity	Cost
Tailscale	Secure remote access to all devices, no port forwarding, works through CGNAT	Easy	Free for personal (up to 100 devices)
Caddy reverse proxy	Pretty URLs (ollama.local, plex.local) + automatic HTTPS	Medium	Free
Pi-hole / AdGuard Home	Network-wide ad blocking, DNS sinkhole, telemetry blocking	Easy	Free
WireGuard (alternative to Tailscale)	Self-hosted VPN if you don't trust 3rd parties	Hard	Free
Cloudflare Tunnel	Expose specific services publicly without opening ports	Medium	Free

Integration Patterns

PATTERN

LLM Federation

Open WebUI on Optiplex routes queries to whichever machine has the right model loaded.

Example: Quick question → Optiplex's Qwen 4B. Code task → MacBook's Qwen 14B. Big reasoning task → new PC's 24B.

PATTERN

Mobile-First Access

iPhone hits any service through Tailscale + Open WebUI.

Example: On a coffee shop wifi, ask your local LLM about a clinical question — encrypted to your Optiplex, response stays private.

PATTERN

Distributed Storage

Optiplex serves files; all devices mount via SMB/NFS.

Example: PDFs of papers stored once on Optiplex; accessible from MacBook for review, new PC for RAG indexing.

PATTERN

Backup Chain

Important data on each device → Optiplex (primary backup) → cloud (Backblaze B2 / iDrive / encrypted Drive).

Example: 3-2-1 rule: 3 copies, 2 different media, 1 offsite.

Power Budget

What it costs to run your ecosystem 24/7. At $0.12/kWh national average.

Device	Idle Draw	Peak Draw	Duty Cycle	Monthly Cost
OptiPlex 7050	25W	65W	24/7	$2.16
New PC (idle)	85W	650W	8h/day	$2.45
New PC (LLM inference)	—	450W	2h/day	$3.24
New PC (gaming)	—	600W	1h/day	$2.16
Network gear	15W	20W	24/7	$1.30
UPS overhead	10W	—	24/7	$0.86
Estimated monthly total				~$12

Context

$12/month for a private AI inference server + gaming rig + media server + home automation hub. A single Claude Pro subscription is $20/month. Running your own stack is cheaper than most people think.

07

The Force Multiplier

Automation

Make your devices earn their keep.

Automation should remove drudgery, not create it. Each one earns its keep only if it saves more time than it cost to build — measured over six months, not six days. The best ones run silently and you forget they exist.

Platforms

Optiplex (Docker) · Medium

n8n

Self-hosted workflow automation. Visual node-based editor. 500+ integrations.

Best for: API integrations, scheduled tasks, complex multi-step workflows

Better than Zapier/Make for self-hosted — your data stays local

Optiplex (HAOS in VM, or Docker) · Medium-Hard

Home Assistant

Home automation hub. Manages smart devices + automations.

Best for: Lights, climate, sensors, cameras, voice assistants

Open ecosystem. Local control. Integrates with everything.

Optiplex (Docker) · Medium-Hard

Node-RED

Flow-based programming. Heavier engineering than n8n.

Best for: IoT, MQTT, complex logic flows

When n8n isn't powerful enough

Optiplex (native) · Easy

Cron + Bash/Python

Classic Linux scheduling. Old-school but bulletproof.

Best for: Simple scheduled scripts, backups, data fetching

Zero overhead. No service to manage.

iPhone · Easy

Shortcuts (iOS)

Apple's automation for iPhone.

Best for: Personal automations triggered by location/time/event

Lives where you do. Triggers from Siri, NFC tags, focus modes.

Ten Recipes Worth Building

Medium

Daily Clinical Briefing

Flow: n8n: 6 AM cron → fetch FDA drug shortages RSS + PubMed new in your specialties + r/medicine top posts → Qwen 14B summarizes → email to you

Value: 5 min reading instead of 60 min scrolling

Medium

PDF Auto-Filing

Flow: Folder watch on Downloads → Phi-4 classifies (paper/monograph/admin/other) → moves to right folder + extracts metadata to SQLite

Value: Searchable paper library without manual filing

Medium-Hard

Calendar-Aware LLM Context

Flow: Before meetings: Shortcut grabs attendees → searches your notes for prior interactions → summary to your Apple Watch

Value: Walk into every meeting prepared

Medium

Vancomycin Calc Validation Bot

Flow: When you push new code to TDM repo → run test suite with 50 known cases → if any fail, halt deployment + Slack alert

Value: Never ship a TDM bug to production

Easy

Backup Verification

Flow: Weekly: restic check on Optiplex → if errors, email + push notification. Monthly: actually restore a random file to verify integrity.

Value: Backups you can trust

Medium

Smart Home Wakeup

Flow: Home Assistant: alarm goes off → lights gradient to warm → coffee maker on → news briefing on speaker → blinds open after 10 min

Value: Morning routine that runs itself

Easy

PubMed Watch

Flow: Cron: daily PubMed search for new vancomycin/AUC papers → Qwen summarizes abstracts → markdown file in Obsidian vault → tagged for review

Value: Stay current on YOUR research interests automatically

Easy

Drug Shortage Tracker

Flow: Cron: FDA drug shortage JSON pull → diff against yesterday → if formulary drugs affected → Slack + email

Value: Know about shortages before pharmacy meeting

Medium

Energy/Cost Monitoring

Flow: Home Assistant + smart plugs on Optiplex/PC → daily power consumption → monthly dashboard of what costs what

Value: Know if running LLMs 24/7 is worth the electricity

Medium-Hard

Voice-to-Clinical-Note

Flow: iPhone Shortcut: record voice → Whisper transcription → Qwen 14B formats as SOAP note → drops in Drafts app → review/edit/file

Value: Capture clinical observations hands-free

Caveat: NEVER use for actual PHI documentation — this is for personal teaching cases only

Docker Install Stack — Your Optiplex Server

Complete from-scratch setup. Run these sequentially on a fresh Ubuntu 22.04 installation.

Docker Engine — Ubuntu/Debian

# Install Docker Engine (one-liner)
curl -fsSL https://get.docker.com | sh

# Add your user to docker group
sudo usermod -aG docker $USER
newgrp docker

# Verify
docker run hello-world
docker compose version

Portainer — Web UI for all containers

Manage every container via browser at :9000. Essential for the Optiplex.

docker run -d \
  --restart=always \
  -p 9000:9000 \
  -v /var/run/docker.sock:/var/run/docker.sock \
  -v portainer_data:/data \
  --name portainer \
  portainer/portainer-ce:latest

# http://optiplex-ip:9000 → set admin password

n8n — Workflow Automation

# docker-compose.yml
services:
  n8n:
    image: n8nio/n8n
    restart: unless-stopped
    ports: ["5678:5678"]
    volumes:
      - n8n_data:/home/node/.n8n
    environment:
      - N8N_BASIC_AUTH_ACTIVE=true
      - N8N_BASIC_AUTH_USER=admin
      - N8N_BASIC_AUTH_PASSWORD=changeme
      - WEBHOOK_URL=http://optiplex-ip:5678

volumes:
  n8n_data:

docker compose up -d
# http://optiplex-ip:5678

Ollama — On the Optiplex (LAN accessible)

# Install on Ubuntu
curl -fsSL https://ollama.ai/install.sh | sh

# Expose on LAN — edit the systemd service
sudo mkdir -p /etc/systemd/system/ollama.service.d
echo '[Service]
Environment="OLLAMA_HOST=0.0.0.0"' | \
  sudo tee /etc/systemd/system/ollama.service.d/override.conf

sudo systemctl daemon-reload
sudo systemctl restart ollama

# Now call from MacBook:
# curl http://optiplex-ip:11434/v1/models

Home Assistant — Home Automation

mkdir -p ~/homeassistant/config

docker run -d \
  --name homeassistant \
  --privileged \
  --restart=unless-stopped \
  -e TZ=America/New_York \
  -v ~/homeassistant/config:/config \
  --network=host \
  ghcr.io/home-assistant/home-assistant:stable

# http://optiplex-ip:8123

Open WebUI on Optiplex (LAN server)

docker run -d \
  --restart unless-stopped \
  -p 3000:8080 \
  -e OLLAMA_BASE_URL=http://localhost:11434 \
  -v open-webui:/app/backend/data \
  --network=host \
  --name open-webui \
  ghcr.io/open-webui/open-webui:main

# MacBook: http://optiplex-ip:3000
# Sign in → full ChatGPT-like UI against your local models

08

The Library

Media Servers

Self-hosted libraries — own what you watch, listen to, and read.

Self-hosted media is about ownership, organization, and access — not piracy. Buy the content, rip it for your library, stream it everywhere.

Legal Note

Owning physical media (DVDs, Blu-rays, books) gives you broad rights to make backup copies for personal use under most jurisdictions. Sharing or distributing those copies is where it crosses lines. This encyclopedia assumes you're operating in the legal personal-use space.

The Stack

Optiplex (Docker)

Jellyfin

Purpose: Media server (video, music, books)

Fully open source. No subscription. No telemetry. Hardware transcoding free (Plex paywalls this).

Optiplex (Docker)

Plex

Purpose: Media server (more polished UI)

Better remote streaming (built-in NAT traversal). Larger ecosystem. Better metadata matching.

⚠ Telemetry-heavy. Increasing ad-supported content push. Account required.

Cost: Plex Pass ($120 lifetime) needed for hardware transcoding + offline downloads

Optiplex (Docker)

Navidrome

Purpose: Music streaming (Spotify replacement)

Subsonic-compatible. Tiny resource footprint. Excellent mobile clients (Substreamer, play:Sub).

Scenario: Your purchased MP3/FLAC library → access from anywhere

Optiplex (Docker)

AudioBookshelf

Purpose: Audiobook + podcast server

Open source Audible-killer. Tracks progress across devices. Auto-downloads podcasts.

Scenario: Audiobooks you bought (Libro.fm, libraries via Libby exports)

Optiplex (Docker)

Immich

Purpose: Photo backup + management (Google Photos replacement)

Mobile app auto-uploads from iPhone. AI face recognition (local). Album sharing.

Optiplex (Docker)

Calibre-web

Purpose: E-book library

Your books accessible everywhere. Send to Kindle/Kobo. OPDS feed for readers.

Scenario: Your DRM-free purchased ebooks + scanned reference texts

Optiplex (Docker)

Komga / Kavita

Purpose: Comics / manga server

If you read comics. Mobile clients are excellent.

Hardware Considerations

Storage is the biggest spend — plan for 8-16TB total over time
HDDs (WD Red Pro, Seagate IronWolf) for media bulk. NVMe for cache.
Optiplex i7-6700T can transcode 1080p H.264 in real-time via QuickSync. Struggles with 4K/HEVC.
ZFS or BTRFS for data integrity if you go beyond 8TB
Optiplex has limited drive bays — consider external USB enclosure (DAS) or upgrading to a NAS box (Synology DS923+ ~$600) when collection grows
RAID is not backup. Always have offline + offsite backup of irreplaceable content (photos especially).

Library Organization

Movies: /Media/Movies/Movie Name (Year)/Movie Name (Year).mkv
TV: /Media/TV/Show Name/Season XX/Show Name - SXXEXX - Episode Name.mkv
Music: /Media/Music/Artist/Album (Year)/XX - Track.flac
Photos: Let Immich organize by date. Don't manually file.
Audiobooks: /Audiobooks/Author/Series #X - Book Title/
Use *arr stack (Sonarr/Radarr/Lidarr/Readarr) ONLY if you understand the legal landscape in your jurisdiction

09

The Mechanics

Finance

Credit mechanics, financing tools, and the pitfalls that catch smart people.

Validate before financing. Stage rather than splurge. Pay yourself first. Never carry a balance you can't kill quickly. These principles guided this PC plan, but they apply to every major purchase for the rest of your life.

Your Situation

690 currently (you mentioned recent drop). Target 720+ in 6 months by NOT opening new accounts.

What Moves Your Credit Score

Payment history (35%)

Pay everything on time. Set autopay for minimums. One missed payment drops 60-110 points.

Credit utilization (30%)

Total balances ÷ total limits. Keep under 30%, ideal under 10%. The PC build risks this if you concentrate on one card.

Length of history (15%)

Don't close old cards. Average age of accounts matters.

Credit mix (10%)

Having a mix (revolving + installment) helps. Mortgage + cards + car loan = ideal mix.

New credit (10%)

Each hard pull dings 3-5 points temporarily. Multiple in 30 days = bigger ding. Why we limit your build to 2 hard pulls.

Where to Check Your Score

annualcreditreport.com — free actual reports from all 3 bureaus, weekly access since 2023
Credit Karma — free score estimates (VantageScore, not FICO — directionally accurate)
Your bank app — most show FICO score for free monthly (Chase, Discover, Citi all do)
Experian app — free FICO score, alerts, fraud monitoring

Financing Tools

50-60% chance

0% APR Purchase Credit Card

Examples: Wells Fargo Reflect (21mo), Chase Freedom Unlimited (15mo), Citi Simplicity (21mo)

How it works: True 0% APR for promo period on new purchases. NO retroactive interest if not paid off.

Risk: Low — just pay above minimum each month

70-80% chance

Store Card with Deferred Interest

Examples: Best Buy (18-24mo), Microcenter (6mo), Home Depot

How it works: 0% during promo, BUT retroactive interest charged on entire purchase if any balance remains at end

Risk: HIGH if you miss the deadline by even $1

Safety: Pay off 1-2 months BEFORE deadline, never on it

90%+ chance

BNPL — Pay-in-4 (Affirm, Klarna, Afterpay)

How it works: Split purchase into 4 biweekly payments. Usually 0% interest.

Risk: Low IF paid on time. Since 2025, Klarna/Affirm report to bureaus — missed payment now hurts credit.

Best for: Items under $500 that you'll pay off in 6 weeks anyway

Likely approved, but high APR

BNPL — Long-term Financing

How it works: 6-36 month installments. APR varies 0-36% based on credit.

Risk: Higher — at 690 credit, your APR likely 10-25%, NOT 0%

⚠ 0% offers often only show at checkout AFTER approval — don't assume you'll get 0%

Possible at 12-18% APR. Credit union usually best.

Personal Loan

Examples: SoFi, LightStream, Marcus, your credit union

How it works: Fixed payments, fixed APR, predictable

Risk: Lowest — predictable, no compounding

Best for: Consolidating multiple high-rate debts

60-70% chance

0% Intro Balance Transfer

How it works: Move existing debt to a new card at 0% for 15-21 months. Usually 3-5% transfer fee.

Risk: Same deferred-interest risk as store cards (sometimes — read fine print)

Best for: Strategic move if you already have credit card debt

Pitfalls

PITFALL

Deferred Interest Retroactive Charge

Scenario: Best Buy 18mo financing. Pay $999 GPU down to $1 by month 18. Forget to pay the dollar. Get charged 30% APR × $999 × 18mo retroactively.

Cost: $300-500 unexpected charge

Prevention: Set autopay for minimum + manual payments above. Pay off 2 months before deadline.

PITFALL

Multiple BNPL Stack

Scenario: 3 Klarna + 2 Affirm + 1 Afterpay simultaneously. Lose track of due dates.

Cost: Missed payments → 60+ point credit drop + 30% APR on balances

Prevention: Max 2 active BNPL at any time. Track due dates in single calendar.

PITFALL

Minimum Payment Death Spiral

Scenario: $5,000 on a 24% APR card. Pay minimum ($100). Takes 14 years to pay off. Pay $7,500 in interest.

Cost: 150% of original purchase in interest

Prevention: Never carry credit card balances beyond promo period. Treat min payment as minimum, not target.

PITFALL

Annual Fee Renewal

Scenario: Opened card for signup bonus. Forgot to cancel. Year 2 charges $95 AF you didn't budget for.

Cost: $95-695 per card

Prevention: Calendar reminder 11 months after opening. Downgrade to no-fee version or cancel.

PITFALL

Co-signed Debt

Scenario: Helped a family member. They miss payments. Your credit destroyed.

Cost: 100+ point credit drop + legal liability

Prevention: Never co-sign for anyone you can't afford to pay off entirely yourself.

The Operating Principles

Pay yourself first — automatic transfer to savings BEFORE you see your paycheck
Emergency fund = 3-6 months of expenses, untouchable
Validate purchases by waiting 30 days for anything over $500 (you did this with the PC — good)
Test before buy — could you use this for 30 days for free/cheap? (Like Optiplex LLM testing before $4K PC)
Total cost > sticker price — include financing fees, AF, time-cost of management
If you can't pay cash, you can't afford it — financing is a tool, not a license

Income Expansion

The same infrastructure you're building for learning can generate revenue. The Optiplex runs today. The new PC multiplies throughput. Every side gig below uses skills you already have or are actively building.

The Compound Principle

Each project sharpens skills that make the next project faster. Clinical writing improves your pharmacy practice. Automation consulting teaches you tools you use at home. The flywheel is the point.

Tier 1 — Start This Week (Optiplex + MacBook)

Zero additional investment. Uses hardware and skills you have right now.

Clinical + LLM · $500-2,000/mo

Medical Writing & Formulary Reviews

What: Drug monographs, formulary evaluations, P&T committee summaries, medication use evaluations. Hospitals and PBMs outsource this constantly.

Your edge: Clinical pharmacist who can use local LLMs for first-draft literature synthesis, then apply expert judgment. 3x faster than manual.

Tools: Qwen3 14B (literature synthesis) + PubMed API + your clinical expertise

First step: Write one sample drug monograph for a recently approved medication. Post on LinkedIn. Reach out to 5 community hospitals without a dedicated drug info pharmacist.

Revenue model

$200-500 per monograph. $1,000-3,000 per formulary review. Recurring if you land a quarterly P&T contract.

Clinical · $300-1,500/mo

CE/CME Content Development

What: Create continuing education modules for pharmacists and nurses. ACPE-accredited providers always need content experts.

Your edge: LLMs draft case studies and assessment questions. You provide clinical accuracy and ACPE formatting expertise.

Tools: Local LLM for draft generation + Canva/LaTeX for slides

First step: Contact 3 ACPE providers (PharmCon, Power-Pak, FreeCE) about their content pipeline. Offer a 1-hour module on a trending topic (GLP-1 agonists, vancomycin AUC monitoring).

Revenue model

$500-2,000 per CE module. Some providers pay royalties per completion.

Tech · $200-800/mo

Automation Consulting for Small Practices

What: Set up n8n workflows, self-hosted tools, and basic automation for independent pharmacies, small clinics, dental offices.

Your edge: You understand clinical workflows AND tech. Most IT consultants don't know pharmacy. Most pharmacists don't know Docker.

Tools: n8n on your Optiplex (demo environment) + Tailscale for remote setup

First step: Automate one workflow at your own workplace. Document the before/after (time saved, errors prevented). That's your case study.

Revenue model

$500-1,500 per setup + $50-200/mo maintenance retainer. 3-5 clients = meaningful passive income.

Content · $100-500/mo

Technical Writing & Tutorials

What: Blog posts, tutorials, and guides about local LLMs, self-hosting, or clinical informatics. Medium, Dev.to, Substack, or your own site.

Your edge: "Clinical pharmacist who runs local LLMs" is a unique perspective. The intersection is underserved.

Tools: Your daily experience + any writing platform

First step: Write "How I Use Local LLMs in Clinical Pharmacy (Without Sending PHI to the Cloud)" — that title alone gets clicks.

Revenue model

Medium Partner Program, Substack paid subscriptions, or sponsored posts. Slow build but compounds with audience.

Watch: The Pharmacist-Tech Intersection

A Day in the Life of an Informatics Pharmacist

Career Path Pharmacy + Tech

Tier 2 — Month 3+ (Validated Skills)

Requires the foundation from Tier 1 plus some proof of work. Higher revenue ceiling.

Clinical + Tech · $2,000-5,000/mo

Clinical Decision Support Tools

What: Build calculators, dosing tools, or clinical dashboards. Vancomycin AUC calculator. Antibiogram visualizer. Renal dose adjustor.

Your edge: You ARE the domain expert. Most dev shops building health tools have zero clinical pharmacists on staff.

Tools: FastAPI/Flask + your clinical knowledge + local LLM for code assistance

First step: Build your vancomycin AUC calculator as a web app. Open-source it. Present it at your state pharmacy association meeting. That talk becomes your sales pitch.

Revenue model

SaaS: $10-50/user/mo for institutional licenses. Or sell as a consulting engagement ($5K-20K per build). Open-source version drives leads.

Tech · $1,000-4,000/mo

Self-Hosted Infrastructure Setup

What: Set up Jellyfin, Immich, Home Assistant, Nextcloud, Pi-hole for privacy-conscious professionals. White-glove home server builds.

Your edge: You've built this exact stack. Your Optiplex is a living demo.

Tools: Docker Compose templates + your Optiplex as reference + Tailscale for remote admin

First step: Create a "Home Server Starter Kit" — a polished docker-compose repo with docs. Share on r/selfhosted. First 3 clients come from there.

Revenue model

$500-1,500 per setup. Hardware markup (you source the Optiplex). $75/mo support retainer. Referral network builds fast.

Clinical · $1,500-4,000/mo

Pharmacy Informatics Consulting

What: Help hospitals optimize EHR workflows, build clinical rules, improve alert fatigue, design order sets. The intersection of pharmacy + IT that every health system needs.

Your edge: Clinical pharmacist who codes. This is one of the most in-demand skill combos in healthcare right now.

Tools: Your clinical license + programming skills + knowledge of FHIR/HL7

First step: Get CPIP (Certified Professional in Health Informatics) or take a health informatics certificate. Build one EHR optimization case study at your current job.

Revenue model

$75-150/hr consulting. Some firms hire part-time remote. This can become a full career pivot if you want it to.

AI + Clinical · $1,000-3,000/mo

LLM Fine-Tuning & Validation Services

What: Help healthcare orgs evaluate, validate, and fine-tune LLMs for clinical use. Run your "Generalized Vancomycin Test" protocol as a paid service.

Your edge: You've already built the validation framework (Pillar 02). Most orgs want to use AI but don't know how to validate it safely.

Tools: Your validation protocol + local GPU for testing + structured report templates

First step: Validate 5 models against your clinical test battery. Publish results as a white paper. Present at ASHP Midyear or a health-AI conference.

Revenue model

$2,000-10,000 per validation engagement. Recurring as models update quarterly.

Investment Fundamentals

Your $350/month savings habit is the foundation. Once the PC is built and debt-free (Month 15), that $350/month redirects to wealth building. Here's the playbook.

The Priority Stack

Money flows in this exact order. Don't skip levels.

L1

Foundation

Emergency Fund

3-6 months expenses in HYSA. This is untouchable. Currently earning 4.5-5% APY at Marcus, Ally, Discover, or Wealthfront. Your $350/mo savings during PC build months goes here first.

Status: protect at all costs

L2

Employer Match

401(k) / 403(b) Match

Contribute at least enough to get full employer match. This is literally free money — 50-100% instant return. If your employer matches 4%, contribute 4% minimum.

Free money — never leave this on the table

L3

High-Interest Debt

Kill Toxic Debt

Pay off anything above 8% APR aggressively. Credit cards (24%), personal loans (15%), store cards. No investment consistently beats 24% guaranteed return of paying off debt.

Debt-free except mortgage/student loans

L4

Tax-Advantaged

Roth IRA / HSA

Max Roth IRA ($7,000/yr in 2026) and HSA if eligible ($4,300 individual). Roth grows tax-free forever. HSA is triple-tax-advantaged — the best account in the tax code.

Roth IRA $583/mo HSA $358/mo

Tax-free growth engine running

L5

Growth

Taxable Brokerage

Everything above goes into a simple 3-fund portfolio. VTI (US total market) + VXUS (international) + BND (bonds). Set allocation based on age and risk tolerance. Automate monthly buys.

Wealth compounds while you sleep

Where to Open Accounts

Best overall

Fidelity

Zero-fee index funds (FZROX, FZILX). No minimums. Excellent app. Roth IRA + brokerage + HSA all in one place. Cash management account replaces a bank.

Best for simplicity

Vanguard

The original index fund company. VTI/VXUS/BND are the gold standard. Interface is dated but reliable. Best if you want to set-and-forget.

Best UI + learning

Schwab

Modern interface. Good research tools. Free stock slices for learning. Merged with TD Ameritrade — strong platform.

The Simple Portfolio

Bogleheads 3-Fund Portfolio — Ultimate Guide

Investing Index Funds

Simple Investing for Beginners — 3-Fund Portfolio

Beginner Getting Started

The 3-Fund Portfolio

This is what most financial advisors charge 1% AUM to do. You can do it yourself for 0.03% expense ratio.

Fund	Ticker	Allocation (age 25-35)	Expense Ratio	What It Holds
US Total Market	VTI / FZROX	60%	0.03% / 0.00%	Every US public company (3,700+)
International	VXUS / FZILX	25%	0.07% / 0.00%	Every non-US developed + emerging market
Bonds	BND / FXNAX	15%	0.03% / 0.03%	US investment-grade bonds (stability)

Rebalance annually (sell winners, buy losers to maintain target %). As you age, shift bonds up 1% per year. That's the entire strategy. It beats 90% of actively managed funds over 20 years.

Project Starter Kits

Actionable blueprints for each growth vector. Each kit lists what you need, what you build first, and how it compounds into the next level.

Growth Vector

Clinical Practice Enhancement

Build Sequence

Week 1: Set up Ollama + Open WebUI on Optiplex. Run your first DDx exercise with DeepSeek R1 14B
Week 2-4: Build vancomycin AUC calculator (Python/Flask). Test against 20 known cases
Month 2: Create RAG pipeline over your institution's antibiogram + IDSA guidelines
Month 3: Present tool at department meeting. Collect feedback. Iterate
Month 4: Submit abstract to state pharmacy conference
Month 6: Publish case series on clinical LLM use in pharmacy practice

Compounds into: Informatics consulting, CE content, conference speaking, clinical tool SaaS

Growth Vector

Technical Skill Stacking

Build Sequence

Week 1: Complete one project with Devstral/Qwen Coder on your Optiplex. Push to GitHub
Month 1: Set up full self-hosted stack (Jellyfin, Immich, Caddy, Pi-hole). Document everything
Month 2: Build 3 n8n automations that save you real time. Measure hours saved
Month 3: Contribute to one open-source project (Open WebUI, Ollama, a clinical tool)
Month 4: Create your first Docker Compose template repo. Share on r/selfhosted
Month 6: First paid automation client (from your case study + network)

Compounds into: Freelance dev work, self-hosted consulting, SaaS products, open-source reputation

Growth Vector

Financial Independence Engine

Build Sequence

Week 1: Open HYSA if not done. Set up $350/mo auto-transfer
Month 1: Open Roth IRA (Fidelity). Set up $100/mo auto-invest into FZROX
Month 3: Review employer 401(k) — are you getting full match? Adjust if not
Month 6: First side gig revenue hits. Funnel 50% to Roth, 50% to brokerage
Month 12: PC build complete. Redirect $350/mo → investments
Month 15: Debt-free. All discretionary income → wealth building

Compounds into: Financial independence. $350/mo in VTI from age 30 = ~$850K by 60 at historical returns

Growth Vector

Autonomous Systems & Self-Improvement

Build Sequence

Week 1: Set up daily briefing automation (n8n → RSS → LLM summary → email)
Month 1: Build Obsidian vault with daily notes. Local LLM indexes and connects ideas
Month 2: Create personal dashboard (Grafana or custom) — track habits, spending, project progress
Month 3: Set up PubMed watch + drug shortage tracker automations
Month 4: Build voice-to-note pipeline (Whisper → Qwen → structured notes)
Month 6: Your ecosystem runs 10+ automations silently. You're the curator, not the operator

Compounds into: Every automation frees time for higher-value work. The system improves itself as you add to it

The Credential Stack

Strategic certifications that multiply your value at each intersection.

Credential	Cost	Time	What It Unlocks
Board Certified Pharmacotherapy (BCPS)	$400	Exam prep: 3-6 mo	Clinical credibility. Required for many clinical positions. Higher pay tier.
CompTIA Security+ / Linux+	$400	Self-study: 2-3 mo	IT credibility. Opens informatics consulting. Validates self-hosted infrastructure skills.
Health Informatics Certificate	$2,000-5,000	6-12 mo (part-time)	Formal bridge between clinical + tech. AMIA-recognized programs. Career pivot enabler.
AWS / GCP Cloud Cert	$300	Self-study: 1-2 mo	Cloud credibility. Pairs with self-hosted knowledge for consulting. Most healthcare is moving to cloud.
Project Management (CAPM/PMP)	$400-600	2-4 mo	Consulting credibility. Required by many healthcare orgs for informatics roles.

Strategy

Don't collect credentials for their own sake. Each one should unlock a specific revenue stream or career move you've already identified. BCPS + informatics certificate + a portfolio of clinical tools = a $140K+ pharmacy informatics position.

Monthly Income Target Roadmap

Timeline	Side Income Target	Primary Sources	Reinvestment
Months 1-3	$0-200/mo	1 medical writing gig, 1 blog post	100% → HYSA / Roth IRA
Months 4-6	$300-800/mo	Recurring writing + 1 automation client	50% invest, 50% reinvest in tools/certs
Months 7-12	$800-2,000/mo	CE content + consulting + tool revenue	50% invest, 30% reinvest, 20% lifestyle
Year 2+	$2,000-5,000/mo	Multiple streams compounding	Auto-invest the majority. You've built the machine

The Point

This PC build isn't a $4,000 expense. It's a $4,000 investment in infrastructure that generates returns — skills, tools, revenue, and compounding knowledge — for years. The ROI is the person you become while building it.

10

The Intelligence Layer

AI Intel

State of the frontier — models, benchmarks, breakthroughs, and how to stay current.

AI research workstation — The frontier moves fast. This tab moves with it.

The pace of AI in 2024–2026 is without precedent in software history. Models double in capability roughly every six months. Staying current isn't optional — it determines which problems you can solve and how.

Model Frontier — May 2026

Proprietary cloud and open-weights models worth knowing. Context = max tokens.

Model	Lab	Tier	Context	Modality	Best For
Claude Opus 4.7	Anthropic	Frontier	1M	Text, Vision, Code	Deep reasoning, long-context, agentic tasks (what runs this site)
Claude Sonnet 4.6	Anthropic	Frontier	200K	Text, Vision, Code	Balanced speed + quality. Best daily driver cloud model.
GPT-4.1	OpenAI	Frontier	1M	Text, Vision, Code	Instruction following, function calling, coding tasks
o3	OpenAI	Frontier	200K	Text, Code	Hard math, competition problems, PhD-level reasoning
Gemini 2.5 Pro	Google	Frontier	2M	Text, Vision, Audio, Video	Multimodal, massive context windows, research synthesis
Llama 4 Maverick 400B	Meta	Strong	1M	Text, Vision	Open weights MoE. Best open model for most tasks.
Qwen3 235B-A22B	Alibaba	Strong	128K	Text, Code	Open MoE near-frontier. Free weights. Self-host on high-VRAM server.
DeepSeek R1	DeepSeek	Strong	128K	Text, Code	Open reasoning model. Visible CoT. Distilled 14B runs locally.
Qwen3 14B / 32B	Alibaba	Local	128K	Text, Code	Your primary local models. Excellent on 16–24GB VRAM.
Devstral 24B	Mistral	Local	128K	Code	Best open coding agent. Edits files, runs tools. Pairs with VS Code.
Phi-4 14B	Microsoft	Local	16K	Text	Exceptional reasoning for 14B size. Fast on 8GB VRAM.

Benchmark Snapshot — What the Tests Actually Measure

Benchmark	Tests	Why It Matters	Leader (2026)
GPQA Diamond	PhD-level science (human experts ~69%)	Hard reasoning ceiling — can't be crammed	o3 / Gemini 2.5 Pro (~87%)
AIME 2024/25	Math Olympiad competition problems	Symbolic + numeric reasoning under pressure	o3 (~96%)
SWE-bench Verified	Real GitHub issues resolved end-to-end	Practical coding — the metric that matters for agents	Claude Opus 4.7 (~72%)
MMLU Pro	57 academic disciplines, harder questions	Broad knowledge breadth across domains	GPT-4.1 / Gemini 2.5 Pro (~91%)
HumanEval	Python function generation from docstrings	Basic coding — now saturated at 99%	Multiple models (benchmark exhausted)
LM Arena (Chatbot Arena)	Human blind preference comparisons, live	Real-world signal — what people actually prefer	lmarena.ai — updated continuously

How to Keep Up — The Essential Stack

Daily Papers

Hugging Face Daily Papers

Every significant ML paper with community notes. Best signal-to-noise for new research. huggingface.co/papers

Live Benchmark

LM Arena

Human-preference model rankings updated continuously from millions of blind comparisons. Ground truth for "which model is actually best." lmarena.ai

Open Models Ranking

Open LLM Leaderboard

Best open-weights models ranked by standardized benchmarks. Check before choosing your next local model. HuggingFace H4 space.

Newsletter

The Batch — deeplearning.ai

Andrew Ng's weekly AI digest. Consistently excellent signal-to-noise on what actually matters vs. hype cycles. Free subscription.

Blog

Simon Willison's Weblog

Practical LLM explorations from a developer's perspective. Best technical writing on what LLMs can actually do week to week. simonwillison.net

Deep Dives

Interconnects Newsletter

Nathan Lambert's RLHF and post-training deep dives. Better than any academic course for understanding how frontier models are actually built. interconnects.ai

Community

r/LocalLLaMA

Best community for local model news, benchmarks, tricks, and hardware discussions. First to post new GGUF model releases and benchmark results.

Twitter/X Follows

Signal Accounts

@ak92501 (HF paper summaries daily), @karpathy (fundamentals), @ylecun (alt views), @simonw (practical), @AnthropicAI, @OpenAI for releases.

Research DB

Papers With Code

State-of-the-art tracking with code repos linked. Best for finding what's replicable vs. theoretical. paperswithcode.com

Key Concepts — The Technical Vocabulary

Architecture Foundation

Transformers & Attention

The architecture behind every major LLM since 2017. Self-attention lets each token attend to all others — no recurrence, fully parallelizable on GPU. Scaled to billions of parameters via pretraining on internet text.

Read: "Attention is All You Need" (Vaswani et al., 2017) — the paper that started it all.

Training Technique

RLHF / DPO — Making Models Helpful

Reinforcement Learning from Human Feedback turns a raw pretrained model into an assistant. Humans rank outputs; a reward model learns preferences; the policy is fine-tuned. DPO (Direct Preference Optimization) achieves similar results without a separate reward model — simpler, often better.

This is what makes Claude helpful, not just fluent.

Deployment Pattern

RAG — Retrieval-Augmented Generation

Give a model a search tool: query a vector DB → retrieve relevant text chunks → inject into prompt → generate grounded answers. Solves hallucination for knowledge-bounded domains better than fine-tuning in most production scenarios.

Local stack: Ollama + ChromaDB + AnythingLLM

Efficiency Technique

Quantization — GGUF/GPTQ

Reduce model precision from FP32/BF16 to INT8/INT4. A 14B model at Q8 needs ~16GB VRAM; at Q4, ~9GB. Quality drop is modest for Q8, acceptable at Q4. Q2 is too aggressive. GGUF is the standard for llama.cpp/Ollama.

Rule: use Q8 if it fits, Q4 otherwise. Never Q2 for clinical tasks.

Architecture Pattern

Mixture of Experts (MoE)

Route each token through a subset of "expert" networks rather than the full model. A 235B MoE model activates ~22B parameters per forward pass — matching a 32B dense model's compute while having far more total capacity. Used in Qwen3 235B, Llama 4 Maverick, Mixtral.

Tradeoff: high total VRAM to load, but fast inference per token.

Frontier Capability

Agents & Tool Use

LLMs as planners that call external tools in loops: web search, code execution, file I/O, API calls. The model outputs structured tool calls; the runtime executes them; results feed back into context. Claude Code, AutoGen, LangGraph, and CrewAI build on this pattern.

2026 frontier: multi-agent systems coordinating autonomously over hours.

Research Radar — 2024–2025 Papers Worth Knowing

Paper / Release	Date	Why It Matters
DeepSeek R1 (DeepSeek)	Jan 2025	Open reasoning model matching o1. Visible chain-of-thought. Proved closed-source reasoning models could be replicated with open weights.
Llama 4 (Meta)	Apr 2025	MoE architecture, 10M token context Scout variant. Democratized frontier-class open weights. Changed calculus on what's runnable locally.
Qwen3 (Alibaba)	May 2025	235B MoE open weights beating GPT-4o on multiple benchmarks. Freely available. Distilled 14B/32B excellent for your local setup.
Flash Attention 2/3	2023–24	IO-aware attention algorithm. 2–4× faster inference with identical outputs. Now standard in every serious LLM runtime.
Chinchilla Scaling Laws (Hoffmann et al.)	2022	Optimal model:data ratio for compute budget. Showed GPT-3 era models were undertrained. Compute-optimal training is the standard now.
LoRA / QLoRA	2021–23	Fine-tune 7B+ models on a consumer GPU via low-rank adapters. Reduces trainable params by 10,000×. Enables personal domain fine-tuning.
Constitutional AI (Anthropic)	2022	Safety via written principles rather than labeling every preference. Backbone of Claude's alignment approach. Widely influential.

AI Timeline — Key Milestones

2017

Transformers — "Attention is All You Need." The paper that made modern AI possible.

2020

GPT-3 (175B) — Few-shot learning emerges from scale. AI becomes commercially relevant for the first time.

Nov 2022

ChatGPT — RLHF turns GPT-3.5 into an assistant. 100M users in 60 days. The public AI moment begins.

2023

GPT-4, Llama, Claude 2 — Multimodal models, open weights, capable local inference. The democratization phase begins.

Jan 2025

DeepSeek R1 — Open reasoning model from a Chinese lab matching OpenAI o1. Reshapes assumptions about frontier AI moats.

Apr–May 2025

Llama 4 + Qwen3 — Open-weights models approach frontier quality. 32B models available for local inference. Gap to closed models narrows dramatically.

2026

Agent era — Models running multi-hour autonomous tasks. Claude Code. Operator frameworks. AI coding productivity multiplied 10×+. You are here.

10

The Wire

Live Feed

Real-time aggregation from the sources that matter. Deals, hardware drops, LLM releases, clinical alerts.

Seven feeds, one view. Deals from r/buildapcsales surface price drops on your exact parts list. r/LocalLLaMA catches new model releases before they hit the benchmarks. Ars and Tom's cover the analysis layer. All fetched server-side, cached 15 minutes, zero tracking.

Fetching feeds from 7 sources…

Sources

DEALS

r/buildapcsales

Community-curated PC component deals. The fastest way to catch price drops on GPUs, SSDs, RAM.

LLM & AI

r/LocalLLaMA

Local LLM community. New model releases, quantization experiments, hardware benchmarks.

LLM & AI

Hugging Face Blog

Official releases and deep dives on new models, datasets, and ML techniques.

HARDWARE

r/hardware

Hardware news and analysis. Product launches, benchmark leaks, industry moves.

HARDWARE

Tom's Hardware

Reviews and benchmarks. The reference for GPU/CPU performance data.

TECH

Ars Technica

Long-form tech journalism. Deep analysis, not just news.

TECH

r/selfhosted

Self-hosting community. Docker stacks, home servers, privacy-first tools.

11

The Log

Journal

Log what works, what doesn't, what you learn. This is your evidence base for the Month 3 checkpoint decision.

Your Validation Journal. Write freely. Tag what mattered. Read it again at the Month 3 checkpoint — your future self will know whether the plan should hold, accelerate, or pivot.

New Entry

Entries

PRIVATE

Harvard CS50P · Python

Learning Python

Your personal course notes, code exercises, and progress tracker. Not visible in navigation.

Access This Section

Navigate directly: add #cs50p to the URL. Or press ⌘+Shift+K (Mac) / Ctrl+Shift+K (Windows/Linux). Notes auto-save to your local storage and sync to KV.

Course Progress 0 / 10 weeks

Week 0 Functions, Variables Todo ▶

Topics

print(), input()
Variables and types: str, int, float, bool
Defining functions with def
Arguments and return values
String methods: .strip(), .lower(), .upper()
f-strings for output formatting

Key Patterns

def greet(name):
    return f"Hello, {name}!"

name = input("Name: ").strip().title()
print(greet(name))

# Type conversion
age = int(input("Age: "))
gpa = float(input("GPA: "))
print(f"In 10 years you'll be {age + 10}")

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pset0 — indoor.py, playback.py, faces.py

Week 1 Conditionals Todo ▶

Topics

if / elif / else
Comparison operators: ==, !=, <, >, <=, >=
Boolean operators: and, or, not
match statements (Python 3.10+)

Key Patterns

grade = int(input("Grade: "))

match grade:
    case g if g >= 90: print("A")
    case g if g >= 80: print("B")
    case g if g >= 70: print("C")
    case _:             print("F")

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pset1 — deep.py, bank.py, meal.py

Week 2 Loops Todo ▶

Topics

while loops, for loops
range(), enumerate(), zip()
List comprehensions
break, continue, pass
Iterating dicts with .items()

Key Patterns

# List comprehension
evens = [x for x in range(20) if x % 2 == 0]

# Dict iteration
scores = {"Alice": 95, "Bob": 87}
for name, grade in scores.items():
    print(f"{name}: {grade}")

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pset2 — camel.py, coke.py, twttr.py, plates.py

Week 3 Exceptions Todo ▶

Topics

try / except / else / finally
ValueError, TypeError, KeyError, IndexError
raise — creating custom exceptions
Exception hierarchy

Key Patterns

def get_int(prompt):
    while True:
        try:
            return int(input(prompt))
        except ValueError:
            print("Please enter an integer.")

age = get_int("Age: ")

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pset3 — fuel.py, felipes.py, grocery.py, outdated.py

Week 4 Libraries Todo ▶

Topics

import, from … import, import as
Standard library: random, statistics, sys, os, math
PyPI packages with pip install
requests — HTTP calls to APIs
sys.argv for CLI arguments

Key Patterns

import random
import requests

# Random choice
coin = random.choice(["heads", "tails"])

# HTTP request
r = requests.get("https://api.github.com/zen")
print(r.text)

pip install requests pyfiglet emoji
pip list  # see installed packages

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pset4 — emojize.py, figlet.py, adieu.py, game.py, bitcoin.py

Week 5 Unit Tests Todo ▶

Topics

pytest framework
Test functions named test_*
assert statements
Testing exceptions with pytest.raises
Running: pytest -v

Key Patterns

# test_calculator.py
from calculator import square
import pytest

def test_positive():
    assert square(3) == 9

def test_negative():
    assert square(-3) == 9

def test_zero():
    assert square(0) == 0

def test_type_error():
    with pytest.raises(TypeError):
        square("x")

pytest test_calculator.py -v
pytest --tb=short  # shorter tracebacks

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pset5 — test_fuel.py, test_bank.py, test_plates.py

Week 6 File I/O Todo ▶

Topics

open(), read(), readlines(), write()
with statement — context manager
csv module: DictReader, DictWriter
Pillow for image manipulation

Key Patterns

import csv

# Write
with open("students.csv", "w", newline="") as f:
    writer = csv.DictWriter(f, fieldnames=["name", "grade"])
    writer.writeheader()
    writer.writerow({"name": "Alice", "grade": 95})

# Read
with open("students.csv") as f:
    for row in csv.DictReader(f):
        print(row["name"], row["grade"])

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pset6 — lines.py, pizza.py, scourgify.py, shirt.py

Week 7 Regular Expressions Todo ▶

Topics

re module: search, match, fullmatch, sub, findall
Character classes: \d, \w, \s, .
Quantifiers: *, +, ?, {n,m}
Groups () and named groups (?P<name>…)
Anchors: ^, $

Key Patterns

import re

# Validate email
pattern = r"^[\w.+-]+@[\w-]+\.[\w.]+$"
if re.fullmatch(pattern, email):
    print("Valid")

# Named capture groups
m = re.search(
    r"(?P\u003cyear\u003e\d{4})-(?P\u003cmonth\u003e\d{2})",
    "2026-05"
)
if m: print(m.group("year"))  # 2026

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pset7 — numb3rs.py, watch.py, working.py, um.py

Week 8 Object-Oriented Programming Todo ▶

Topics

class, __init__, self
Instance vs. class attributes
__str__, __repr__, __eq__ — dunder methods
Inheritance and super()
@property, @classmethod, @staticmethod

Key Patterns

class Student:
    def __init__(self, name: str, grade: int):
        self.name = name
        self.grade = grade

    @property
    def letter(self) -> str:
        if self.grade >= 90: return "A"
        if self.grade >= 80: return "B"
        return "F"

    def __str__(self) -> str:
        return f"{self.name}: {self.letter}"

alice = Student("Alice", 95)
print(alice)  # Alice: A

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pset8 — seasons.py, cookie.py, jar.py

Week 9 Et Cetera — Advanced Python Todo ▶

Topics

Type hints and annotations
Generators with yield
Decorators with @
map(), filter(), sorted() with lambdas
Unpacking: *args, **kwargs
Context managers (__enter__, __exit__)

Key Patterns

# Generator
def fibonacci():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b

first_10 = [next(fibonacci()) for _ in range(10)]

# Decorator
def log(func):
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__}")
        return func(*args, **kwargs)
    return wrapper

@log
def greet(name: str) -> str:
    return f"Hello, {name}!"

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Final Project — build something meaningful

Python Quick Reference

Types: str, int, float, bool, list, tuple, dict, set, None
Mutability: list/dict/set = mutable · str/tuple = immutable
Scope: LEGB — Local → Enclosing → Global → Built-in

Comprehensions:
[x for x in iter if cond] — list
{k: v for k, v in d.items()} — dict
{x for x in list} — set