R&D BAY

Lab

The home laboratory - where 25+ years of ops discipline meets local machine learning. Everything on this bench gets measured, versioned, and documented, because "it works on my GPU" deserves the same rigor as production.

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Local LLMs

Running open-weight models on local hardware: inference servers, quantization trade-offs, context-length limits - and what it actually takes to self-host a capable assistant.

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GPU Benchmarks

Measuring what local hardware really delivers: tokens per second, VRAM ceilings, batch sizes, thermals under sustained load. Numbers, not vibes.

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Experiments

Fine-tuning small models, RAG pipelines, and testing what transfers from Kaggle notebooks to the home rack - with the failures logged alongside the wins.

// write-ups land in notes and on the blog as the numbers stabilize