Chameleon introduces a disaggregated architecture that efficiently combines LLM and vector search accelerators to power retrieval-augmented language models (RALMs).

• Enables smaller, more efficient language models while maintaining generation quality
• Reduces computational requirements by orders of magnitude
• Integrates specialized hardware components in a flexible system architecture
• Optimizes performance for both LLM inference and vector database retrieval operations

This innovation represents a significant engineering advancement for AI infrastructure, addressing the growing demand for more efficient and specialized systems to support context-aware AI applications at scale.

Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Models