Topic
5090 Reports
28 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
My local LLM eval hid four token caps
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.
- 5 min read
The 26B Model Hit the Cap. The 8B Finished.
Same code task, same 512-token cap. Ollama's done_reason showed one local model finished and one got cut off. Tokens per second hid it.
- 5 min read
Unload Local LLMs After Every Test
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.
- 5 min read
How I Benchmark Local LLMs Before I Trust Them
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.
- 5 min read
How I Test a 30B Local Model Before I Load It
A 30B local model can fit on paper and still fail the job. This test plan checks memory, tool use, speed, and repeatability first.
- 5 min read
Why Local LLM Benchmarks Need Power Data
A local LLM benchmark that reports only tokens per second hides the operating cost. My RTX 5090 run shows what power data adds.
- 5 min read
VRAM Fit Is Not Runtime Support
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.
- 6 min read
Search Old Results Before Publishing an LLM Test
An independent QA pass caught my second post about the same Ollama batch sweep. Here is the duplicate check I now run before publishing an LLM result.
- 5 min read
Why I Test 3 Workloads Before Sizing a Local LLM
One local LLM speed number hides the work behind it. My RTX 5090 sweep shows why short generation, long context, and code need separate rates.
- 5 min read
Test Retrieval Before Your Local LLM Writes
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.
- 5 min read
Why I Did Not Promote My Smaller Local Model
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.
- 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.
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