Polish DeepSpeed blog post #1
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Nov 14, 2023
| - vLLM’s mission is to build the fastest and easiest-to-use open-source LLM inference and serving engine. It is Apache 2.0 and community-owned with broad model and optimization support. | ||
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| - vLLM matches DeepSpeed's speed in common scenarios and surpasses it when handling longer outputs. | ||
| - DeepSpeed only outperforms vLLM in scenarios with long prompts and short outputs, due to its Dynamic SplitFuse optimization. This optimization is on vLLM’s roadmap. |
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| - DeepSpeed only outperforms vLLM in scenarios with long prompts and short outputs, due to its Dynamic SplitFuse optimization. This optimization is on vLLM’s roadmap. | |
| - DeepSpeed only outperforms vLLM in scenarios with long prompts and short outputs with its Dynamic SplitFuse optimization. This optimization is on vLLM’s roadmap. |
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I think the current one is a bit better since it only has 1 with?
| In our blog today, we'll elucidate the specific scenarios where the Dynamic SplitFuse technique is advantageous, noting that these cases are relatively limited. | ||
| For the majority of workloads, vLLM is faster than (or performs comparably to) DeepSpeed MII. | ||
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| In this post, we will discuss the difference between the two systems, share our benchmarks, and discuss future steps. |
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I think this is redundant. In the previous sentence we already said "In this blog, ..."
Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>
Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>
Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>
Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>
Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>
Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>
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Jan 27, 2025
Update 2025-01-12-intro-to-llama-stack-with-vllm.md
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