Explore Databricks' innovative approach to optimizing vLLM for enhanced LLM inference performance in this Ray Summit 2024 presentation. Discover how Megha Agarwal and her team at Databricks (MosaicML) tackle the challenges of GPU blocking operations during decoding steps, which can significantly impact performance for large models. Learn about their solutions to reduce GPU idle time and accelerate quantization using custom kernels. Gain valuable insights into future optimization areas and best practices for benchmarking LLM deployments. Ideal for organizations and developers working on large-scale LLM projects, this talk offers practical strategies to improve inference efficiency and reduce costs in LLM serving products.
Databricks' vLLM Optimization for Cost-Effective LLM Inference - Ray Summit 2024
Overview
Syllabus
Databricks' vLLM Optimization for Cost-Effective LLM Inference | Ray Summit 2024
Taught by
Anyscale
Related Courses
-
Efficiently Serving LLMs
-
The State of vLLM - Advancements in LLM Inference and Serving
-
Accelerated LLM Inference with Anyscale - Ray Summit 2024
-
Optimizing LLM Inference with AWS Trainium, Ray, vLLM, and Anyscale
-
The Evolution of Multi-GPU Inference in vLLM
-
Optimizing vLLM for Intel CPUs and XPUs - Ray Summit 2024
