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.
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.
Read MoreSeparate context windows, key-value cache, prefix caching, and persistent application memory before choosing an LLM design. The context-path test connects evidence position, token-weighted reuse, cache capacity, concurrency, latency, retention, and accepted output instead of treating a larger advertised window as dependable memory.
Read MoreTreat parameter count, dense or mixture-of-experts architecture, and quantization as configuration fields rather than value scores. This guide shows CTOs what each number can describe, what it cannot prove, and how to compare exact artifacts on matched workloads, hardware, service, and recovery conditions.
Read MoreUse 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.
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