SquirrelGlitch
SquirrelGlitch

Let the squirrel brain go to work and create some fun stuff.

Working notes on local LLM infrastructure, agent pipelines, and shop hardware — measured, with the data.

Research

  1. Three vLLM models on one RTX 3090, measured

    Qwen3.8-27B, Ornith-1.5-35B-A3B and DiffusionGemma-26B-A4B, each booted through llama-swap on the same workstation, timed with the same harness, and judged by whether the code they returned actually runs. With a small-model round and a controlled prompt-and-sampling re-test.

    RTX 3090 · 24 GB local-llmvllmllama-swapbenchmark
  2. Putting a coding model on the RTX 3090

    What is actually worth running on a dedicated 24 GB Ampere card in late August 2026, measured rather than advertised: llama.cpp + llama-swap against vLLM W4A16, the Ampere-specific traps, and how it wires into the tools already running on the workstation.

    RTX 3090 · 24 GB local-llmllama.cppvllmampere