What's this about?
DeepSeek combines cross-layer cache reuse, sparse attention and FP4 caching in a 552B-parameter MoE designed for input-heavy, long-horizon agent workloads.
What exactly is KV cache?
It stores attention keys and values from prior tokens so a model need not recompute them at every step. Compression saves memory and bandwidth but must preserve enough information for quality.
Why does this matter?
Author-reported preprint; 137 HF upvotes at fetch time.
- Million-token contexts shift cost toward prefill and storage.
- Lower active parameters do not equal a small model footprint.
- Quality, throughput and hardware claims need independent serving tests.