Engineering leader building LLM inference systems.
Every number I publish came from code that was executed.
Deep in LLM inference performance — benchmark harness and quantization studies on an L40S, published as I go.
Approach
Inference systems, and the distributed platforms under them.
Fifteen years of building systems that carry load. Today I lead a team of eight across six products, and my own work sits on inference — serving, benchmarking, quantization economics — on top of a long background in distributed AWS platforms.
Verification gates, not vibes.
Every code block is executed before it ships, and every published number traces back to printed output I can point at. If a claim cannot be reproduced from a run, it does not go out.
A sequenced curriculum, one article at a time.
I work through machine learning in order, in public. The writing is the study log: what I ran, what it measured, and what it cost.
Focus areas
Inference serving
Getting models to answer under real load: batching behaviour, memory headroom, and the shape of the latency curve as concurrency climbs.
Inference lab
Illustrative model, not measured output. It exists to show the shape of the trade-off — quantize, and weights shrink while throughput rises; raise concurrency, and throughput and latency climb together. Real numbers appear only in the articles, next to the runs that produced them.
Writing
Published as I go.
I publish the inference work one article at a time on dev.to — harness design, benchmark results, and the cost arithmetic behind them.