AI inference glossary

Benchmark metrics

Warm-cache benchmark

In plain English

A test run with data already resident in some cache levels, which may reduce traffic to slower memory.

Technical definition

A warm-cache benchmark measures execution after prior access has populated relevant caches, with the useful residency determined by working-set size and cache behavior.

Engineering details

Repeating a small operator can reuse inputs that a production workload would fetch from a lower memory level. Warmup can also stabilize compilation and runtime state, but those effects are distinct from data-cache residency. A reproducible report identifies what was repeated and which working set fits.

Why it matters

Warm-cache tests can isolate compute or scheduling behavior while understating cold-memory costs. Comparing them with a streaming-memory test without disclosing the difference can misidentify the bottleneck.

How to read it in InferenceX

The Huawei port labels its TileLang operator comparison as warm-cache testing. That condition should accompany quoted speedups; it does not establish end-to-end serving throughput or peak HBM bandwidth.

See the concept in real benchmarks

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