DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and
  DeepSpeed-Inference
The deployment and scaling of large language models (LLMs) have become
critical as they permeate various applications, demanding high-throughput and
low-latency serving systems. Existing frameworks struggle to balance these
requirements, especially for workloads with long prompts. This paper introduces
DeepSpeed-FastGen, a system that employs Dynamic SplitFuse, a novel prompt and
generation composition strategy, to deliver up to 2.3x higher effective
throughput, 2x lower latency on average, and up to 3.7x lower (token-level)
tail latency, compared to state-of-the-art systems like vLLM. We leverage a
synergistic combination of DeepSpeed-MII and DeepSpeed-Inference to provide an
efficient and easy-to-use serving system for LLMs. DeepSpeed-FastGen's advanced
implementation supports a range of models and offers both non-persistent and
persistent deployment options, catering to diverse user scenarios from
interactive sessions to long-running applications. We present a detailed
benchmarking methodology, analyze the performance through latency-throughput
curves, and investigate scalability via load balancing. Our evaluations
demonstrate substantial improvements in throughput and latency across various
models and hardware configurations. We discuss our roadmap for future
enhancements, including broader model support and new hardware backends. The
DeepSpeed-FastGen code is readily available for community engagement and
contribution.
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Connor Holmes
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2024-01-18T09:20:21.356Z
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Connor Holmes
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Masahiro Tanaka
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2024-01-18T09:20:40.022Z
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Masahiro Tanaka
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Michael Wyatt
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2024-01-18T09:20:54.201Z
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Michael Wyatt
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Ammar Ahmad Awan
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2024-01-18T09:21:21.108Z
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Ammar Awan
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Jeff Rasley
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2024-01-18T09:21:27.489Z
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Jeff Rasley
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Samyam Rajbhandari
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2024-01-18T09:21:33.367Z
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Samyam Rajbhandari
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samyam
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Reza Yazdani Aminabadi
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Heyang Qin
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2024-01-18T09:21:47.716Z
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Heyang Qin
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Arash Bakhtiari
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2024-01-29T08:26:26.348Z
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Arash Bakhtiari
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Lev Kurilenko
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2024-01-18T09:22:24.973Z
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Lev Kurilenko
user
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Yuxiong He
DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and
  DeepSpeed-Inference
The deployment and scaling of large language models (LLMs) have become
critical as they permeate various applications, demanding high-throughput and
low-latency serving systems. Existing frameworks struggle to balance these
requirements, especially for workloads with long prompts. This paper introduces
DeepSpeed-FastGen, a system that employs Dynamic SplitFuse, a novel prompt and
generation composition strategy, to deliver up to 2.3x higher effective
throughput, 2x lower latency on average, and up to 3.7x lower (token-level)
tail latency, compared to state-of-the-art systems like vLLM. We leverage a
synergistic combination of DeepSpeed-MII and DeepSpeed-Inference to provide an
efficient and easy-to-use serving system for LLMs. DeepSpeed-FastGen's advanced
implementation supports a range of models and offers both non-persistent and
persistent deployment options, catering to diverse user scenarios from
interactive sessions to long-running applications. We present a detailed
benchmarking methodology, analyze the performance through latency-throughput
curves, and investigate scalability via load balancing. Our evaluations
demonstrate substantial improvements in throughput and latency across various
models and hardware configurations. We discuss our roadmap for future
enhancements, including broader model support and new hardware backends. The
DeepSpeed-FastGen code is readily available for community engagement and
contribution.
DeepSpeed-FastGen enhances the deployment of large language models by introducing Dynamic SplitFuse, improving throughput and latency compared to existing frameworks.
DeepSpeed-FastGen Dynamic SplitFuse DeepSpeed-MII DeepSpeed-Inference prompt and generation composition non-persistent deployment persistent deployment latency-throughput curves load balancing hardware backends