#5090reports

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.

My 5090-rig eval uses four output caps: 50, 180, 192, and 256 tokens. A score without the cap is not a model result. Record the cap on every row.

A local model test is not over when text appears. I unload the model, read idle VRAM, and record the result before I start another run.

A local LLM benchmark should end with a decision. I record task quality, tokens per second, VRAM, power, and failure state before I add a model.

A local model can fit in VRAM, download cleanly, and still fail before the first token. My 5090 test adds runtime support as a separate gate.

A local LLM benchmark that reports only tokens per second hides the operating cost. My RTX 5090 run shows what power data adds.

I make my local LLM show its source plan before it writes. The preview gate catches weak retrieval while the fix is still cheap and easy to inspect.

My smaller local model existed and ran, but it did not beat the baseline. Here is the promotion gate I use before changing a working local AI route.

Claude Science keeps data on local systems but sends selected context to Claude. Here is the owned-GPU version, with measured RTX 5090 costs.

Blog charts need correct labels. I tightened layout geometry, char budgets, claims checks, and vision logs so a bad PNG cannot ship quietly.

A bigger local model will not fix a stuck runtime. Add a bounded inference doctor first, then trust the benchmark.

Local LLMs are useful when the loop proves the output, not when the benchmark looks good. This is the small gate I use before a local coding agent gets more rope.