Efficient memory management for large language model serving with pagedattention. com/zr2bm/github-action-ssh-deploy.

3655835 Corpus ID: 269265579; Deferred Continuous Batching in Resource-Efficient Large Language Model Serving @article{He2024DeferredCB, title={Deferred Continuous Batching in Resource-Efficient Large Language Model Serving}, author={Yongjun He and Yao Lu and Gustavo Alonso}, journal={Proceedings of the 4th Workshop on Machine Learning and Systems}, year={2024}, url={https vLLM: A high-throughput and memory-efficient serving engine for large language models, accelerated with PagedAttention. 🔥🔥[PagedAttention] Efficient Memory Management for Large Language Model Serving with PagedAttention(@UC Berkeley etc) ⭐️⭐️: 2023. 611--626. Oct 17, 2023. Apr 27, 2024 · This work proposes PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems, and builds vLLM, an LLM serving system that achieves near-zero waste in KV cache memory and flexible sharing of KV Cache within and across requests to further reduce memory usage. vLLM supports popular LLMs such as GPT (Brown et al. May 28, 2024 · Efficient Memory Management for Large Language Model Serving with PagedAttention. Efficient Memory Management for Large Language Model Serving with PagedAttention. UC Berkeley nikhiljha@berkeley. Efficient Memory Management for Large Language Model Serving with PagedAttention, SOSP,23, LLM,Machine Learning,, ---. code project. g. edu. SOSP ’23, page 611–626, New York, NY, USA, 2023. 5: The key three blocks, and the. The memory for the KV cache (red) is (de)allocated per serving request. Larger models amplify Pensieve’s advantage over the baselines because the amount of com-putation grows faster than the memory usage of KV-tokens. Gonzalez and Hao Zhang and Ion Stoica}, booktitle = {Proceedings of the ACM SIGOPS 29th Symposium on Efficient Memory Management for Large Language Model Serving with PagedAttention Woosuk Kwon , Zhuohan Li , Siyuan Zhuang , Ying Sheng , Lianmin Zheng , Cody Hao Yu , Joseph E. Figure 11 shows the performance of Pensieve for larger mod-els, OPT-66B and Llama 2-70B, when run on four GPUs us-ing the ShareGPT dataset. In Proceedings of the 29th Symposium on Operating Systems Principles (SOSP '23). 3613165. 1145/3642970. Requests (e. acm. Gonzalez1 Hao Zhang4 Ion Stoica1 1UC Berkeley 2Stanford University 3Independent Researcher 4UC San Diego Abstract High throughput serving of large language models (LLMs) Efficient Memory Management for Large Language Model Serving with PagedAttention. A small amount of memory (yellow) is used ephemerally for activation. vAttention thus enables one to use state-of-the art attention kernels out-of-the-box by adding Apr 22, 2024 · DOI: 10. 12 Sep 2023 · Woosuk Kwon , Zhuohan Li , Siyuan Zhuang , Ying Sheng , Lianmin Zheng , Cody Hao Yu , Joseph E. Inspired by demand paging, vLLM proposed PagedAttention to enable dynamic memory allocation for KV-cache. A practical large language model (LLM) service may involve a long system prompt, which specifies the instructions code project. vLLM's architecture includes a centralized scheduler and distributed GPU workers, and it shows a 2-4x throughput improvement over state-of-the-art systems in evaluations. In ICML. . But there is a shortage of memory because of how large the large language models have become. Existing system such as hugging face transformer. 1. Inspired by OS-based virtual memory systems, vLLM proposed PagedAttention to enable dynamic memory allocation for KV-cache. Jul 4, 2024 · type: metadata version: Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. 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. Sep 12, 2023 · vLLM is a system that leverages PagedAttention to manage memory with nearly zero wasted KV cache, enhancing throughput and model serving flexibility. 基于Transformer的大语言模型(LLM) LLM服务和自回归生成. Aug 22, 2023 · Efficient Memory Management for Large Language Model Serving with PagedAttention by Woosuk Kwon (UC Berkeley), Zhuohan Li (UC Berkeley), Siyuan Zhuang (UC Berkeley), Ying Sheng (Stanford University), Lianmin Zheng (UC Berkeley), Cody Hao Yu (Independent Researcher), Joseph Gonzalez (UC Berkeley), Hao Zhang (UC Berkeley and UC San Diego) and Ion Efficient memory management for large language model serving with pagedattention W Kwon, Z Li, S Zhuang, Y Sheng, L Zheng, CH Yu, J Gonzalez, H Zhang, Proceedings of the 29th Symposium on Operating Systems Principles, 611-626 , 2023 Sep 12, 2023 · PagedAttention is a novel algorithm that allows for non-contiguous allocation of KV cache memory in serving large language models (LLMs), reducing waste and improving efficiency. 06180 ( 2023) last updated on 2024-07-04 21:53 CEST by the. 3 Multi-GPU Serving Performance. Large Language Models (LLMs) demonstrate substantial potential across a diverse array of domains via request serving. Gonzalez and Haotong Zhang See full list on dl. KV cache非常大; 复杂的解码算法 Oct 17, 2023 · Efficient Memory Management for Large Language Model Serving with PagedAttention. Prior systems reserved memory for the KV-cache ahead-of-time, resulting in wasted capacity due to internal fragmentation. Our evaluations show that vLLM improves the throughput of popular LLMs by 2--4× with the same level of latency compared to the state-of-the-art Sep 12, 2023 · BlockLLM is presented, a serving system that exploits the potential of sharing components among fine-tuned LLM models to offer an efficient and flexible solution for LLM workloads, and reduces memory and storage footprints and improves computation efficiency. Next, We describe the PagedAttention algorithm in §4. 3613165 Corpus ID: 261697361; Efficient Memory Management for Large Language Model Serving with PagedAttention @article{Kwon2023EfficientMM, title={Efficient Memory Management for Large Language Model Serving with PagedAttention}, author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. May 7, 2024 · In this paper, we propose vAttention, a new approach for dynamic KV-cache memory management. Efficient management of attention key and value memory with PagedAttention. Gonzalez and Hao Zhang and Ion Stoica}, booktitle = {Proceedings of the ACM SIGOPS 29th Symposium on Feb 28, 2024 · In this work, we build vLLM, a high-throughput distributed LLM serving engine on top of PagedAttention that achieves near-zero waste in KV cache memory. In Proceedings of the 29th Symposium on Operating Systems Principles. 09 [KV Cache FP8 + WINT4] Exploration on LLM inference performance optimization(@HPC4AI) ⚠️: ⭐️: 2023. Association for Computing Machinery. This paper aims to reduce the monetary cost for serving LLMs by leveraging preemptible GPU instances on modern clouds, which offer accesses to spare GPU resources at a much cheaper price than regular instances but may be preempted by the cloud provider at any time. all metadata released as under. Gonzalez and Haotong Zhang @inproceedings {kwon2023efficient, title = {Efficient Memory Management for Large Language Model Serving with PagedAttention}, author = {Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Kevin Wang. The key innovation is the PagedAttention algorithm, which efficiently handles attention keys and values in non-contiguous memory spaces. Coordinated and efficient huge page management with ingens. In contrast to PagedAttention, vAttention retains KV-cache in contiguous virtual memory and leverages low-level system support for demand pag-ing, that already 20 hours ago · However, they still don't address the specific challenges of LLM serving, such as efficient memory management for large models. In contrast to PagedAttention, vAttention stores KV-cache in contiguous virtual memory and leverages OS support for on-demand allocation of physical memory. , 2020), OPT (Zhang et al Jul 4, 2024 · metadata version: 2024-07-04. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically. 2023. Vicuna: An Open-source Chatbot Impressing GPT-4 with 90%* Chatgpt Quality. The evaluations from the paper show that, “vLLM improves the LLM serving throughput by 2-4× compared to the state-of-the-art systems…without affecting the model accuracy at all. ACM, 611--626. Gonzalez and Hao Zhang and Ion Stoica}, booktitle = {Proceedings of the ACM SIGOPS 29th Symposium on Sep 12, 2023 · DOI: 10. Improving Larg e Languag e Model Throughput with Efficient Long- Term Memory Manag ement. Prior systems used to reserve KV-cache memory ahead-of-time that resulted in wasted capacity due to internal fragmentation. [45] Youngjin Kwon, Hangchen Yu, Simon Peter, Christopher J. DOI: 10. Sep 12, 2023 · High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. , 2048 tokens) due to unknown output length. This approach helps minimize fragmentation and optimizes memory usage, allowing for faster and more efficient model serving. 胡世鹏. last updated on 2024-07-04 21:53 CEST by the. However, due to the lack of open-sourced LLM serving workloads, these systems are frequently evaluated under unrealistic workload assumptions. {"payload":{"allShortcutsEnabled":false,"fileTree":{"notes":{"items":[{"name":"images","path":"notes/images","contentType":"directory"},{"name":"1-Spock. org persist in GPU memory throughout serving. ”. 10: 🔥[TensorRT-LLM KV Cache FP8] NVIDIA TensorRT LLM(@NVIDIA) [TensorRT-LLM] 이 시스템은 요청을 효율적으로 처리하기 위한 새로운 접근 방식인 'PagedAttention' 알고리즘을 안녕하세요 딥러닝 논문읽기 모임 입니다! 오늘은 Apr 27, 2024 · The high computational and memory requirements of generative large language models (LLMs) make it challenging to serve them cheaply. The following images illustrate KV cache manager efectively manages the KV cache in a paged fashion, enabled by PagedAttention. Gonzalez and Hao Zhang and Ion Stoica}, booktitle = {Proceedings of the ACM SIGOPS 29th Symposium on Sep 18, 2023 · A large KV cache is thus a limitation when dealing with LLM inference. PagedAttention in Fig. vLLM is a system that leverages PagedAttention to achieve high throughput and flexibility in LLM serving, with 2-4x improvement over state-of-the-art systems. Feb 28, 2024 · In summary, the PagedAttention algorithm allows the KV blocks to be stored in non-contiguous physical memory, which enables more flexible paged memory management in vLLM. This means 2 things: Decode phase doesn’t saturate compute. PagedAttention Algorithm: Inspired by virtual memory and paging in operating systems, it partitions the KV cache into blocks that are not stored in contiguous memory, allowing more flexible and efficient memory management. Mar 27, 2024 · Introduction. PagedAttention, which is inspired by the classic techniques of virtual memory and paging in operating systems, significantly reduces the memory consumption of LLMs, while vLLM, an open-source library, enables a fast and simple serving of LLMs using PagedAttention. Gonzalez, Hao Zhang, Ion Stoica May 7, 2024 · Efficient management of GPU memory is essential for high throughput LLM inference. AlpaServe : Use model parallelism to accelerate deep learning serving, even when models fit a single GPU. ---. Jan 5, 2024 · Infinite-LLM disaggregates attention layers from an LLM's inference process, facilitating flexible and independent resource scheduling that optimizes computational performance and enhances memory utilization jointly. Jun 4, 2024 · The main concept behind vLLM is to optimize memory management and inference speed for large language models. May 27, 2024 · This work proposes PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems, and builds vLLM, an LLM serving system that achieves near-zero waste in KV cache memory and flexible sharing of KV Cache within and across requests to further reduce memory usage. It solves the long-standing problem of request Efficient Memory Management for Large Language Model Serving with PagedAttention by Woosuk Kwon (UC Berkeley), Zhuohan Li (UC Berkeley), Siyuan Zhuang (UC Berkeley), Ying Sheng (Stanford University), Lianmin Zheng (UC Berkeley), Cody Hao Yu (Independent Researcher), Joseph Gonzalez (UC Berkeley), Hao Zhang (UC San Diego) and Ion Stoica (UC Efficient memory management for large language model serving with pagedattention. 1145/3600006. SOSP 2023. Leviathan et al. On top of it, we build vLLM, an LLM serving •If even a single attention head is too large, or we want to split it across multiple chips to improve latency, how can vLLM support sharding the KV cache? •The fundamental bottlenecks faced by LLM serving, memory capacity due to large model weights, and memory bandwidth due to low compute intensity of auto-regressive decoding, remain unsolved. UC Berkeley kevwang@berkeley. edu Abstract— Delivering low latency and high through- put when serving larg e languag e models (LLMs) re- quires the intellig ent batching and caching of inputs and DOI: 10. [2] Azalia Mirhoseini,Hieu Pham,Quoc V Le,Benoit Steiner,Rasmus Larsen,Yuefeng 了解知乎专栏的精彩内容和作者分享的见解。 Thus, the PagedAttention model leads to software complexity, portability issues, redundancy and ineficiency. Large language models (LLMs) have demonstrated exceptional performance across a wide range of language-related tasks. see also: | |. Decode phase is memory bound. Sep 12, 2023 · When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. In this paper, we propose vAttention for dynamic KV-cache memory management. Gonzalez , Hao Zhang , Ion Stoica Feb 15, 2024 · Efficient Memory Management for Large Language Model Serving with PagedAttention vLLM implements a paging mechanism for the attention computation called pagedAttention. It’s challenging to manage memory efficiently. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles, 2023. ). Liu et al. Sep 12, 2023 · DOI: 10. In order to generate for more prompts, we can make bigger batches. (2023) Yaniv Leviathan, Matan Kalman, and Yossi Matias. Woosuk Kwon*, Zhuohan Li*, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Yu, Joey Gonzalez, Hao Zhang, Ion Stoica. md","path 20 hours ago · vLLM Memory Management: The vLLM framework uses a Key-Value (KV) cache to manage memory more efficiently. CoRR abs/2309. 6. Gonzalez and Hao Zhang and Ion Stoica}, booktitle = {Proceedings of the ACM SIGOPS 29th Symposium on Paper Efficient Memory Management for Large Language Model Serving with PagedAttention Woosuk Kwon (UC Berkeley), Zhuohan Li (UC Berkeley), Siyuan Zhuang (UC Berkeley), Ying Sheng (Stanford University), Lianmin Zheng (UC Berkeley), Cody Hao Yu (Independent Researcher), Joseph Gonzalez (UC Berkeley), Hao Zhang (UC San Diego), Ion Stoica (UC @inproceedings {kwon2023efficient, title = {Efficient Memory Management for Large Language Model Serving with PagedAttention}, author = {Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. In Proceedings of the 29th Symposium on Operating Systems Principles, SOSP 2023, Koblenz, Germany, October 23-26, 2023. Illustration of the PagedAttention algorithm, where the attention key and values vectors are stored as non-contiguous blocks in the memory. In short, the idea behind PagedAttention is to create contiguous virtual blocks mapped to physical blocks in the GPU memory. vLLM uses block-level memory management and preemptive request scheduling that are co-designed with PagedAttention. Nov 1, 2023 · 本文是论文Efficient Memory Management for Large Language Model Serving with PagedAttention的解读。 目录. Conference: SOSP '23: 29th Symposium on Operating Systems Principles Sep 12, 2023 · Efficient Memory Management for Large Language Model Serving with PagedAttention. To Oct 23, 2023 · Efficient Memory Management for Large Language Model Serving with PagedAttention. Reporter. (20-40% used for store request) Internal fragmentation: over-allocated (e. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles. Gonzalez and Haotong Zhang May 16, 2024 · PagedAttention is an algorithm delineated in the academic paper Efficient Memory Management for Large Language Model Serving with PagedAttention. KV cache manager efectively manages the KV cache in a paged fashion, enabled by PagedAttention. This approach minimizes memory fragmentation and enhances resource utilization. Gonzalez and Hao Zhang and Ion Stoica}, booktitle = {Proceedings of the ACM SIGOPS 29th Symposium on Nov 17, 2023 · Credit: Efficient Memory Management for Large Language Model Serving with PagedAttention Inspired by paging in operating systems, the PagedAttention algorithm enables storing continuous keys and values in noncontiguous space in memory. Efficient memory management for large language model serving with pagedattention. Gonzalez and Haotong Zhang Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, Ion Stoica: Efficient Memory Management for Large Language Model Serving with PagedAttention. Tags. vAttention thus enables one to use state-of-the art attention kernels out-of-the-box by adding support for dynamic allocation of physical memory without having to re-write their code. Moreover, as pointed out by Kwon et al. However, the computational intensity and memory consumption of Jul 15, 2024 · Efficient memory management for large language model serving with pagedattention. I’ve already described it in my previous article about vLLM , but let’s have another look at it, with the support of the illustrations and the results published in their @inproceedings {kwon2023efficient, title = {Efficient Memory Management for Large Language Model Serving with PagedAttention}, author = {Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. LTT + {}^{+} start_FLOATSUPERSCRIPT + end_FLOATSUPERSCRIPT [23] Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, and Song Han. Oct 31, 2023 · But because of this, any time taken in this process is strictly time spent on memory activities. In Proceedings of the 29th Symposium on Operating Systems Principles, pages 611–626, 2023. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems. SOSP 2023: 611-626. However, to Jun 11, 2024 · About. in Efficient Memory Management for Large Language Model Serving with PagedAttention, the current trend in the GPU market is characterized by a stable growth in the computation speed (FLOPS) and a much slower increase of the memory Jun 23, 2023 · The goal is to store key-value tensors more efficiently in the non-contiguous spaces of the GPU VRAM. SOSP 23. Abstract; Introduction; Background. May 7, 2024 · Efficient use of GPU memory is essential for high throughput LLM inference. The success of Large Language Models (LLMs) across a wide range of applications and use Efficient Memory Management for Large Language Model Serving with PagedAttention, SOSP, 2023 S-LoRA: Serving Thousands of Concurrent LoRA Adapters, arXiv, 2023 [ Paper ] [ Code ] Petals: Collaborative Inference and Fine-tuning of Large Models, arXiv, 2023 [ Paper ] Sep 12, 2023 · DOI: 10. Efficient Memory Management for Large Language Model Serving with PagedAttention Woosuk Kwon *, Zhuohan Li *, Siyuan Zhuang, Ying Sheng, Lianmin Zheng , Cody Hao Yu, Joseph E. , Request A and Request B) are allocated blocks of the KV cache (KV Block 0, KV Block 1, etc. Gonzalez1 Hao Zhang4 Ion Stoica1 1UC Berkeley 2Stanford University 3Independent Researcher 4UC San Diego Abstract High throughput serving of large language models (LLMs) Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. Apr 15, 2024 · Use memory inefficiently lead to memory waste, and can thus store few requests, which leads low throughtput. Fast inference from transformers via speculative decoding. Gonzalez and Haotong Zhang Explore thought-provoking articles and insights on Zhihu's column platform. Continuous batching of incoming requests. Figure 5. Vicuna : An open-source chatbot impressing GPT-4 with 90% ChatGPT quality. prompt阶段; 自回归生成阶段; LLM的batching技术; LLM serving的内存挑战. Apr 22, 2024 · This paper presents an extensive experimental evaluation that aims to capture the impact of the workload used for evaluation and quantify the benefit derived from higher memory availability, and shows that LLMs can achieve 3× higher throughput compared to text summarization and conversational use cases. High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. Gonzalez and Hao Zhang and Ion Stoica}, booktitle = {Proceedings of the ACM SIGOPS 29th Symposium on On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage. Expand Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. The KV cache manager effectively manages the KV cache in a paged fashion, enabled by PagedAttention. High throughput serving of large language models (LLMs) requires batching sufficiently many Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. vLLM System: A serving system built on top of PagedAttention, featuring block-level memory management and preemptive Feb 22, 2024 · This paper proposes RelayAttention, an attention algorithm that allows reading these hidden states from DRAM exactly once for a batch of input tokens, and has observed significant performance improvements to a production-level system, vLLM, through integration with RelayAttention. Gonzalez , Hao Zhang , Ion Stoica ·. Understanding Memory Management in LLM Serving. vLLM is a fast and easy-to-use library for LLM inference and serving. This approach eliminates fragmentation and improves serving throughout. Gonzalez and Haotong Zhang Jan 31, 2024 · Serving systems for Large Language Models (LLMs) are often optimized to improve quality of service (QoS) and throughput. October 2023. Nikhil Jha. Each block is designed to store key-value pairs’ tensors for a predefined number of tokens. However, as trends continue to push for expanding context Nov 11, 2023 · PagedAttention and vLLM are two technologies that make large language models (LLMs) faster and more efficient. vLLM is fast with: State-of-the-art serving throughput. Fast model execution with CUDA/HIP graph. Gonzalez, Hao Zhang, Ion Stoica: Efficient Memory Management for Large Language Model Serving with PagedAttention. Efficient memory management for large language model serving with pagedattention W Kwon, Z Li, S Zhuang, Y Sheng, L Zheng, CH Yu, J Gonzalez, H Zhang, Proceedings of the 29th Symposium on Operating Systems Principles, 611-626 , 2023 Keep track of the papers I have read and to be read - Efficient Memory Management for Large Language Model Serving with PagedAttention · Issue #9 · Tom-CaoZH/paper_readings Jan 11, 2024 · Improving throughput can significantly decrease the cost of large language model serving by responding to more requests with the same number of GPU resources. Edit social preview. Gonzalez and Hao Zhang and Ion Stoica}, booktitle = {Proceedings of the ACM SIGOPS 29th Symposium on [VLDB '24] Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity [SOSP '23] Efficient Memory Management for Large Language Model Serving with PagedAttention [ICML '23] BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models Oct 17, 2023 · Efficient Memory Management for Large Language Model Serving with PagedAttention. Efficient memory management is critical for serving large language models (LLMs) due to the extensive computational resources required. @inproceedings {kwon2023efficient, title = {Efficient Memory Management for Large Language Model Serving with PagedAttention}, author = {Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Specifically, the KV cache manager manages the physical KV cache memory on the GPU workers through the instructions sent by the centralized scheduler. Rossbach, and Emmett Witchel. Consequently, performance may degrade when these systems are deployed in real-world scenarios. Right: vLLM smooths out the rapid growth curve of KV cache memory seen in existing systems [31, 60], leading to a notable boost in serving throughput. This work presents BurstGPT, an LLM Apr 25, 2024 · A High-Throughput and Memory-Efficient Inference and Serving Engine for LLMs. When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. jp im uv mj js le ae zj ma ot