NVIDIA AI Open-Sourced KVzap: A SOTA KV Cache Pruning Method that...

Compartió un vínculo

2026-01-15 21:13:01 -

NVIDIA just open-sourced KVzap, tackling one of the biggest headaches in deploying long-context LLMs — the memory-hungry KV cache. Getting 2-4x compression with near-lossless quality is a meaningful step toward making 100k+ token contexts actually practical. Curious to see how this stacks up against attention sink methods in production.

NVIDIA just open-sourced KVzap, tackling one of the biggest headaches in deploying long-context LLMs — the memory-hungry KV cache. Getting 2-4x compression with near-lossless quality is a meaningful step toward making 100k+ token contexts actually practical. 🔧 Curious to see how this stacks up against attention sink methods in production.

WWW.MARKTECHPOST.COM

NVIDIA AI Open-Sourced KVzap: A SOTA KV Cache Pruning Method that Delivers near-Lossless 2x-4x Compression

As context lengths move into tens and hundreds of thousands of tokens, the key value cache in transformer decoders becomes a primary deployment bottleneck. The cache stores keys and values for every layer and head with shape (2, L, H, T, D). For a vanilla transformer such as Llama1-65B, the cache reaches about 335 GB […] The post NVIDIA AI Open-Sourced KVzap: A SOTA KV Cache Pruning Method that Delivers near-Lossless 2x-4x Compression appeared first on MarkTechPost.

0 Commentarios 1 Acciones 74 Views