Topic
Ollama
38 posts on ollama, 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
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
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
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.
- 5 min read
Ollama Load Time Can Hide a Fast Local LLM
My RTX 5090 run spent 43.48 seconds on model load and 0.17 seconds on output. Split wall time before you judge local LLM speed.
- 5 min read
A 32 GB GPU Still Needs Host RAM Headroom
My RTX 5090 had 32 GB of VRAM ready, but my local policy stopped Ollama at 7.02 GiB of free host RAM. Here is my preflight before model load.
- 5 min read
49W Average Hid a 338W Burst on Gemma 26B
Gemma 4 26B Q4_K_M averaged 49 W on a long RTX 5090 run and peaked at 338 W. Keep both watt numbers before you compute energy per token.
- 5 min read
How to Calculate Local LLM Energy per Token
My RTX 5090 test shows how watts and output rate become joules per token, and why the faster of two matched settings can waste energy.
- 4 min read
Why a Failed Local LLM Benchmark Row Still Matters
A failed local LLM row marks the test boundary. My RTX 5090 report shows why quality, speed, and settings belong in one receipt.
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