Table of Links Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.4 Application to Other Decoding Scenarios 4.5 Scheduling and Preemption 4.6 Distributed Execution 5 Implementation 6 Evaluation and 6.1 Experimental Setup 6.2 Basic Sampling 6.3 Parallel Sampling and Beam Search 6.4 Shared prefix 6.5 Chatbot 7 Ablation Studies 8 Discussion 9 Related Work 10 Conclusion, Acknowledgement and References 6.4 Shared prefix We explore the effectiveness of vLLM for the case a prefix is shared among different input prompts, as illustrated in Fig. 10. For the model, we use LLaMA-13B [52], which is multilingual. For the workload, we use the WMT16 [4] Englishto-German translation dataset and synthesize two prefixes that include an instruction and a few translation examples. The first prefix includes a single example (i.e., one-shot) while the other prefix includes 5 examples (i.e., few-shot). As shown in Fig. 16 (a), vLLM achieves 1.67× higher throughput than Orca (Oracle) when the one-shot prefix is shared. Furthermore, when more examples are shared (Fig. 16 (b)), vLLM achieves 3.58× higher throughput than Orca (Oracle). This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Woosuk Kwon, UC Berkeley with Equal contribution;
(2) Zhuohan Li, UC Berkeley with Equal contribution;
(3) Siyuan Zhuang, UC Berkeley;
(4) Ying Sheng, UC Berkeley and Stanford University;
(5) Lianmin Zheng, UC Berkeley;
(6) Cody Hao Yu, Independent Researcher;
(7) Cody Hao Yu, Independent Researcher;
(8) Joseph E. Gonzalez, UC Berkeley;
(9) Hao Zhang, UC San Diego;
(10) Ion Stoica, UC Berkeley. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.3 Decoding with PagedAttention and vLLM 4.4 Application to Other Decoding Scenarios 4.4 Application to Other Decoding Scenarios 4.5 Scheduling and Preemption 4.5 Scheduling and Preemption 4.6 Distributed Execution 4.6 Distributed Execution 5 Implementation 5 Implementation 6 Evaluation and 6.1 Experimental Setup 6 Evaluation and 6.1 Experimental Setup 6.2 Basic Sampling 6.2 Basic Sampling 6.3 Parallel Sampling and Beam Search 6.3 Parallel Sampling and Beam Search 6.4 Shared prefix 6.4 Shared prefix 6.5 Chatbot 6.5 Chatbot 7 Ablation Studies 7 Ablation Studies 8 Discussion 8 Discussion 9 Related Work 9 Related Work 10 Conclusion, Acknowledgement and References 10 Conclusion, Acknowledgement and References 6.4 Shared prefix We explore the effectiveness of vLLM for the case a prefix is shared among different input prompts, as illustrated in Fig. 10. For the model, we use LLaMA-13B [52], which is multilingual. For the workload, we use the WMT16 [4] Englishto-German translation dataset and synthesize two prefixes that include an instruction and a few translation examples. The first prefix includes a single example (i.e., one-shot) while the other prefix includes 5 examples (i.e., few-shot). As shown in Fig. 16 (a), vLLM achieves 1.67× higher throughput than Orca (Oracle) when the one-shot prefix is shared. Furthermore, when more examples are shared (Fig. 16 (b)), vLLM achieves 3.58× higher throughput than Orca (Oracle). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Woosuk Kwon, UC Berkeley with Equal contribution; (2) Zhuohan Li, UC Berkeley with Equal contribution; (3) Siyuan Zhuang, UC Berkeley; (4) Ying Sheng, UC Berkeley and Stanford University; (5) Lianmin Zheng, UC Berkeley; (6) Cody Hao Yu, Independent Researcher; (7) Cody Hao Yu, Independent Researcher; (8) Joseph E. Gonzalez, UC Berkeley; (9) Hao Zhang, UC San Diego; (10) Ion Stoica, UC Berkeley. Authors: Authors: (1) Woosuk Kwon, UC Berkeley with Equal contribution; (2) Zhuohan Li, UC Berkeley with Equal contribution; (3) Siyuan Zhuang, UC Berkeley; (4) Ying Sheng, UC Berkeley and Stanford University; (5) Lianmin Zheng, UC Berkeley; (6) Cody Hao Yu, Independent Researcher; (7) Cody Hao Yu, Independent Researcher; (8) Joseph E. Gonzalez, UC Berkeley; (9) Hao Zhang, UC San Diego; (10) Ion Stoica, UC Berkeley.

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