标题：Colossal-AI团队开源SwiftInfer，实现大模型推理性能46%的提升
近日，Colossal-AI团队在MIT成果的基础上，开源了一款名为SwiftInfer的新版本。据悉，这一新版本在大模型推理性能上实现了46%的提升，有效解决了多轮对话推理场景落地应用的低成本、低延迟、高吞吐等需求。
在此之前，MIT的研究团队已经推出了一款名为StreamingLLM的开源方案，该方案可以在不牺牲生成效果、推理速度的前提下，实现多轮对话共400万个token，22.2倍推理速度提升。然而，StreamingLLM使用原生PyTorch实现，对于多轮对话推理场景落地应用的低成本、低延迟、高吞吐等需求仍有优化空间。
为了解决这一问题，Colossal-AI团队基于TensorRT对StreamingLLM进行了优化，推出了SwiftInfer。据了解，SwiftInfer在上线不到3个月时间内，GitHub项目标星已经达到5.7k star，受到了业界的广泛关注和好评。
SwiftInfer的推出，不仅进一步提升了大模型推理性能，还为多轮对话推理场景落地应用提供了更加优质的解决方案。未来，Colossal-AI团队将继续致力于大模型推理技术的研究与优化，为人工智能领域的发展贡献力量。
英语如下：
Title: “Colossal-AI Open Sources SwiftInfer, Achieving 46Title: “Colossal-AI Open Sources SwiftInfer, Achieving 46% Increase in Inference Speed and Optimizing Bottlenecks in Large Model Applications”
Keywords: StreamingLLM, Inference Acceleration, SwiftInfer
Content:
Title: Colossal-AI Team Open Sources SwiftInfer, Achieving a 46% Increase in Inference Performance for Large Models
Recently, the Colossal-AI team has open-sourced a new version of their software called SwiftInfer based on the work done by the MIT team. This new version achieves a 46% increase in inference performance for large models, effectively addressing the needs for low cost, low latency, and high throughput in the deployment of multi-turn dialogue inference scenarios.
Prior to this, the MIT research team had already released an open-source solution called StreamingLLM, which can achieve up to 22.2 times faster inference speed without sacrificing generation quality or inference speed for up to 4 million tokens in multi-turn dialogues. However, StreamingLLM was implemented using native PyTorch, leaving room for optimization in terms of meeting the requirements for low cost, low latency, and high throughput in the deployment of multi-turn dialogue inference scenarios.
To address this issue, the Colossal-AI team optimized StreamingLLM using TensorRT and introduced SwiftInfer. It is reported that within three months of its launch, SwiftInfer has received 5.7k stars on GitHub, indicating widespread attention and positive feedback from the industry.
The introduction of SwiftInfer not only further improves the inference performance of large models but also provides a more optimal solution for the deployment of multi-turn dialogue inference scenarios. In the future, the Colossal-AI team will continue to focus on research and optimization of large model inference technology, contributing to the development of the field of artificial intelligence.
【来源】https://mp.weixin.qq.com/s/fiYSESKcOgZIDe8dpLdAdQ
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