The KV Cache Compression Race: TurboQuant vs OSCAR vs EpiCache
Long-context giant language fashions (LLMs) face a reminiscence bottleneck that has nothing to do with mannequin weights. During decoding, transformers cache the important thing and worth (KV) vectors for each token at each layer in order that they don’t must recompute consideration. This cache grows linearly with sequence size and batch measurement, and at lengthy…
