Editing Openai/691a41cd-2efc-800c-9eff-de439224a90d (section)

===== File family: baseline & performance tests, decode+GEMM CUDA files, quick blob tests, etc. =====

They do things like:
* Baseline benchmarking - Measure pure 3090 performance before any hooks: - Bandwidth - Typical inference/training loops - Behavior under memory pressure
* Decode + GEMM benchmarks - Check the cost of “decompress → matrix multiply” vs “just multiply the full tensor.” - Goal: show that the overhead of decompression is smaller than the savings from moving less data.
* Real blob harnesses - Feed real model blobs (weights, KV cache dumps, activation snapshots) through the pipeline. - Verify bit-exact round-trip: original == decompress(compress(original)).

All of this produces human-readable logs (compression_report, logs directory) with stats like:
* Number of tensors compressed
* Total bytes saved
* Time spent compressing/decompressing
* Speedup vs. baseline

So it’s not just “the math says this will be faster”; you’ve actually instrumented the whole thing.