Chameleon: Adaptive Caching and Scheduling for Many-Adapter LLM Inference Environments

Understand

The widespread adoption of LLMs has driven an exponential rise in their deployment, imposing substantial demands on inference clusters.

• These clusters must handle numerous concurrent queries for different LLM downstream tasks.
• To handle multi-task settings with vast LLM parameter counts, methods like Low-Rank Adaptation (LoRA) enable task-specific fine-tuning while sharing most of the base LLM model across tasks.
• Hence, they allow concurrent task serving with minimal memory requirements.