Summary Efficient Memory Management for Large Language Model Serving arxiv.org
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The paper introduces PagedAttention, an attention algorithm inspired by virtual memory and paging techniques, to efficiently manage memory in large language model serving.
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Key Points
- Efficient Memory Management for Large Language Model Serving is addressed in the paper by proposing PagedAttention, an attention algorithm inspired by virtual memory and paging techniques.
- vLLM significantly improves LLM serving throughput by 2-4x without affecting model accuracy.
- The autoregressive generation phase of large language model serving is memory-bound and underutilizes GPU computation.
- The PagedAttention algorithm allows for non-contiguous storage of attention key and value vectors in memory, overcoming challenges of fragmentation and memory sharing.
- vLLM efficiently manages memory by storing the KV cache of multiple requests in logical and physical blocks, enabling parallel processing and increased hardware utilization.
- The paper introduces the concept of parallel sampling, where multiple samples share the same input prompt and can share the KV cache, saving memory.
- The vLLM engine is developed using Python and C++/CUDA code, with key components written in Python and custom CUDA kernels used.
- vLLM demonstrates high throughput and efficient memory management compared to other models like Orca and FasterTransformer.
Summaries
38 word summary
The paper discusses efficient memory management for large language model (LLM) serving. It introduces PagedAttention, an attention algorithm inspired by virtual memory and paging techniques, which is used to build vLLM, an LLM serving system that reduces memory
38 word summary
The paper presents PagedAttention, an attention algorithm inspired by virtual memory and paging techniques, for efficient memory management in large language model (LLM) serving. This algorithm is used to build vLLM, an LLM serving system that reduces memory
600 word summary
Efficient Memory Management for Large Language Model Serving is addressed in the paper by proposing PagedAttention, an attention algorithm inspired by virtual memory and paging techniques. The algorithm is used to build vLLM, an LLM serving system that reduces memory waste
vLLM significantly improves LLM serving throughput by 2-4x without affecting model accuracy. It addresses challenges in memory allocation and proposes PagedAttention, an attention algorithm that operates on non-contiguous paged memory. vLLM outper
The autoregressive generation phase of large language model serving generates new tokens sequentially. The model takes one token as input and computes the probability of the next token using key and value vectors. This phase is memory-bound and underutilizes GPU computation. Transformers
Efficient memory management is crucial for large language model (LLM) serving. The LLM generates tokens one by one, and the key and value vectors of existing tokens are cached for generating future tokens. However, this caching process leads to memory challenges
The PagedAttention algorithm allows for non-contiguous storage of attention key and value vectors in memory. It overcomes the challenges of fragmentation and memory sharing in large language model serving systems. The algorithm partitions the KV cache into blocks, enabling more flexible memory
vLLM efficiently manages memory for large language model serving by storing the KV cache of multiple requests in logical and physical blocks. This allows for parallel processing and increased hardware utilization, improving throughput. vLLM dynamically assigns new physical blocks to logical blocks as
The paper discusses efficient memory management techniques for large language model serving. The authors introduce the concept of parallel sampling, where multiple samples share the same input prompt and can therefore share the KV cache of the prompt, saving memory. They propose a copy-on-write
Efficient memory management for large language model serving is achieved through the implementation of an all-or-nothing eviction policy, where all blocks of a sequence are either evicted or none are. Sequences within one sequence group are gang-scheduled together due to
This strategy partitions linear layers and uses the SPMD execution schedule. The attention operator is split on the attention head dimension, and each SPMD process handles a subset of attention heads. The vLLM features a single KV cache manager within the centralized scheduler
The vLLM engine is developed using 8.5K lines of Python and 2K lines of C++/CUDA code. Key components, such as the scheduler and block manager, are written in Python, while custom CUDA kernels are used
We evaluate the performance of vLLM with basic sampling on three models and two datasets. On the ShareGPT dataset, vLLM can sustain higher request rates compared to Orca and FasterTransformer while maintaining similar latencies. On the Alp
This paper proposes vLLM, a high-throughput language model serving system with efficient memory management. It introduces PagedAttention, a new attention algorithm that allows attention keys and values to be stored in non-contiguous paged memory. The paper demonstrates
This summary provides a list of references to related research papers and resources mentioned in the document "Efficient Memory Management for Large Language Model Serving." The references include papers on chatbots, language modeling, prediction serving systems, GPU batching, attention mechanisms, GPU
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This document cites several papers and resources related to memory management for large language model serving. The papers mentioned include Megatron-lm, OLLA, Sequence to sequence learning with neural networks, Stanford Alpaca, Llama, Attention is all you