Editing Openai/692db884-c2d8-800c-a357-aa2a0e684fbe (section)

===== It doesn’t just naively zip everything. It uses several GPU-friendly strategies (taken from your compression report): =====
* Zero-run encoding – When activations come out of things like ReLU, huge slices are literal zero. Those get stored as “runs of zeros” instead of full arrays.
* Sparse matrix compression – Pruned / near-sparse weight matrices are stored in “only store non-zeros + where they go” formats.
* Value dictionaries / quantization – If many values repeat or fall into small numeric ranges, it can store them as small integer codes plus a tiny lookup table.
* Batch delta compression – When inputs or internal states across a batch are very similar, it stores “this one is like that one, plus small deltas.”
* LoreToken semantic compression – For structured stuff (prompts, KV cache with repeating patterns, etc.), it uses your LoreToken-style symbolic patterns instead of raw numeric soup.

Each of those is chosen because it:
* Compresses well
* Can be decoded insanely fast on GPU
* Fits the tensor layout the model expects.