Efficient Memory Management for Large Language Model Serving with PagedAttention
arxiv.org · 5,348 words · saved by 1 readers
N/A
Efficient Memory Management for Large Language Model Serving with PagedAttention Woosuk Kwon1,∗ Zhuohan Li1,∗ Siyuan Zhuang1 Ying Sheng1,2 Lianmin Zheng1 Cody Hao Yu3 Joseph E. Gonzalez1 Hao Zhang4 Ion Stoica1 1 UC Berkeley 2 Stanford University 3 Independent Researcher 4 UC San Diego…
related reading
- Paged Attention from First Principles: A View Inside vLLM – Hamza's Bloghamzaelshafie.bearblog.dev
- Introduction to vLLM and PagedAttentionblog.runpod.io
- vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention | vLLM Blogblog.vllm.ai
- Look Ma, No Bubbles! Designing a Low-Latency Megakernel for Llama-1B · Hazy Researchhazyresearch.stanford.edu
- Transformers Inference Optimization Toolset | AstraBlogastralord.github.io
- Inside vLLM: Anatomy of a High-Throughput LLM Inference Systemvllm.ai
- [2412.02252] Compressing KV Cache for Long-Context LLM Inference with Inter-Layer Attention Similarityarxiv.org
- LLM Inference Performance Engineering: Best Practices | Databricks Blogdatabricks.com
- [2603.23516] MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokensarxiv.org
- The Big LLM Architecture Comparisonmagazine.sebastianraschka.com
- [2412.14838] DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMsarxiv.org
- [2205.14135] FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenessarxiv.org