Topic
Hardware
32 posts on hardware — guides and lab notes from real runs on hardware we own. New posts land here automatically. Start anywhere, or grab the copy-paste prompts that ship with them.
- 6 min read
Build an AI Research Workbench on Your Own GPU
Claude Science keeps data on local systems but sends selected context to Claude. Here is the owned-GPU version, with measured RTX 5090 costs.
- 5 min read
Ollama num_batch: 256 Was My RTX 5090 Sweet Spot
I tested four Ollama settings on Gemma 4 26B. num_batch 256 delivered nearly all the speed of 512 while using less power on my RTX 5090.
- 5 min read
Why production AI is moving to open weights
I tested a local-first writing pipeline on an RTX 5090. Gemma 4 writes the draft, deterministic checks catch errors, and frontier QA decides what ships.
- 6 min read
My 8B Model Failed a 400-Word Task
Three Llama 3.1 8B runs missed a 400-word floor. Here is the verifier-driven route that moved long-form synthesis to Gemma 4 26B.
- 6 min read
I Rebuilt 5,383 Embeddings After a Dimension Change
I moved a vault search index from 1,536 to 768 dimensions. Here is why I rebuilt all 5,383 chunks instead of mixing vector spaces.
- 6 min read
Pin Your Local LLM Context Size Before You Build a Router
Changing context size can reload a local model before every request. A measured RTX 5090 sweep shows why context belongs in the routing key.
- 8 min read
I built a self-improving code model on one RTX 5090. Here is what actually worked.
Six pieces, one consumer GPU, no cloud. The honest results: some parts worked, some were flat, and one idea changed everything.
- 5 min read
Will That Local Model Fit? Do the VRAM Math First
A local LLM needs about half a gigabyte of VRAM per billion parameters at Q4, then KV cache and context stack on top. Here is how to know a model fits before you download 40 GB.
- 5 min read
How I Make Local Model Runs Fail Safely On A 5090
A local model run should prove its safety path before it proves a score. Here is the small guardrail loop I use on my RTX 5090 for QLoRA starter work.
- 5 min read
How to Make a Local QLoRA Starter Fail Safely
A local QLoRA starter should prove data, GPU safety, metrics, tests, and blockers before it claims progress. Here is the small loop I use on owned hardware.
- 2 min read
VRAM Calculator: Estimate Local LLM Requirements
Estimate the VRAM required to run local LLMs like Llama 3 with our interactive calculator. Compare quantization levels like Q4 and Q8 to plan your hardware.
- 7 min read
llama.cpp --tensor-split: Run a 70B Across 2 GPUs (2026)
Your 70B model won't fit on one GPU. Here's how llama.cpp --tensor-split, --main-gpu, and --split-mode spread it across two cards on a real consumer rig.
The AI agent build notes
Real costs, real tools, no fluff. M-F when I ship, publish, or learn something worth sending.