Topic
5090 Reports
48 posts on 5090 reports, 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.
- 5 min read
Bonsai 27B vs Gemma 4 E4B: Which Should You Run?
Compare Ternary Bonsai 27B and Gemma 4 E4B for local AI. See VRAM footprints, multimodal support, benchmark scores, and run commands for your setup.
- 5 min read
A second machine will not run a bigger model
NVIDIA's Personal AI Router spreads inference requests across the machines on one network. It does not pool VRAM, and its scheduler counts queued jobs.
- 5 min read
q4_k_m vs q8_0: which GGUF quant should you use?
Compare Q4_K_M and Q8_0 for Llama 3.1 8B. Learn how quantization affects file size, VRAM usage, perplexity, and generation speed on local hardware.
- 5 min read
My Local Blog Writer Drops Private Lines First
My local blog writer reads a daily log. I remove private lines before drafting, then check the output again. Here is what those checks can miss.
- 5 min read
Ollama JSON: Empty Results Are Not Failed Requests
An empty Ollama result can mean no matches or a broken request. I test the response parser so local extraction failures cannot pass as clean results.
- 5 min read
Local or API? Test the task before routing it
Compare a local model with an API on the same task. Record settings, failed attempts, review time and cost before you decide where the workload belongs.
- 5 min read
Log Local LLM Fallbacks Before You Score the Output
My local writer hit a host RAM guard, then Sonnet produced a draft that failed QA. Here is how I separate availability, model identity, and useful output.
- 5 min read
My local daily brief used 0 model calls
On September 4, 2026 my llama3.1:8b daily brief was ready with 0 model calls and 0 seconds. Preview-only file ranking is not a model run.
- 5 min read
Long Context Cost Me VRAM, Not Tokens a Second
A 94x bigger prompt cost llama3.1:8b only 9.7% of its generation rate. VRAM rose 656 MiB, but num_ctx doubled in the same step. Budget memory first.
- 5 min read
3 Tests Before a GGUF Quant Runs Your Coding Agent
A GGUF file fitting in VRAM does not prove it can run your coding agent. Use this local acceptance test for tools, patches, and repeat runs.
- 5 min read
One model logged 14.5 and 6,178 prompt tokens a second
gemma4:26b read 53 prompt tokens at 14.5 tok/s and 4,470 at 6,178.9 on the same RTX 5090 run set. Prompt speed is not one number.
- 5 min read
Split Local LLM Prose and JSON Jobs by Model
My local AI stack routes prose to Gemma and bounded JSON to Qwen. The split keeps model choice tied to the output contract, not one leaderboard.
The AI agent build notes
Real costs, real tools, no fluff. One evidence-backed note on Friday when there is something worth sharing.
Get the requested artifact now, then at most one evidence-backed Local AI Lab Note on Friday when there is something worth sharing. One-click unsubscribe. No sponsored placements. Privacy.