Field notes and decision memos

Private AI Decision Library

Explore evidence-led notes on where AI should run, what agents need after the demonstration, and how technical mechanisms change workflow economics. Claims are bounded to their workload and evidence.

August 30, 2026LLM Serving Inference Engineering AI Performance

Prefill, Decode, and Goodput: Where LLM Inference Actually Bottlenecks

Diagnose LLM serving by separating prefill, decode, queueing, batching, speculative decoding, and external work. The inference measurement record keeps phase metrics tied to representative arrival patterns, tail latency, failures, and accepted workflow goodput so a local optimization cannot masquerade as an operating improvement.

August 27, 2026LLM Benchmarking Private AI Inference Engineering

Operating a Private 27B LLM: What Makes a Benchmark Decision-Grade?

Use a benchmark card to decide when measurements from a private 27B language-model environment are complete, comparable, reproducible, and relevant to architecture. The method records workload, hardware, model representation, cache state, concurrency, acceptance, units, matched baselines, raw outputs, and failures without publishing unsupported performance claims.