SubGen: Token Generation in Sublinear Time and Memory

Understand

Despite the significant success of large language models (LLMs), their extensive memory requirements pose challenges for deploying them in long-context token generation.

• The substantial memory footprint of LLM decoders arises from the necessity to store all previous tokens in the attention module, a requirement imposed by key-value (KV) caching.
• In this work, our focus is on developing an efficient compression technique for the KV cache.
• Empirical evidence indicates a significant clustering tendency within key embeddings in the attention module.