近日，一款名为StreamingLLM的开源方案引起了业界关注。该方案在不到3个月的时间内，在GitHub上获得了5.7k的star，其实验成果在400万token上下文的推理中实现了46%的加速，同时不牺牲生成效果和推理速度。这对于多轮对话场景的落地应用来说，无疑是一个重要的突破。
StreamingLLM的主要优势在于，它可以在不牺牲生成效果和推理速度的前提下，实现多轮对话共400万个token的推理，推理速度提升了22.2倍。这主要得益于其使用原生PyTorch实现，使得其在多轮对话推理场景的落地应用中，具有低成本、低延迟、高吞吐的潜力。
然而，StreamingLLM仍有优化空间。为此，Colossal-AI团队推出了SwiftInfer，这是一个基于TensorRT的StreamingLLM，可以进一步提升大模型推理性能46%，有效解决上述问题。
StreamingLLM的推出，不仅为开源界带来了新的突破，也为大模型推理性能的提升提供了新的可能。未来，随着SwiftInfer的进一步优化，我们有理由相信，大模型推理性能将得到更快的提升，从而推动人工智能技术的发展。
英文翻译：
Recently, an open-source solution called StreamingLLM has attracted industry attention. In less than 3 months, it has received 5.7k stars on GitHub, achieving a 46% acceleration in the inference of 4 million tokens of context without sacrificing generation effects and inference speed. This is undoubtedly an important breakthrough for the implementation of multi-turn dialogue scenarios.
The main advantage of StreamingLLM is that it can achieve the inference of 4 million tokens of multi-turn dialogue without sacrificing generation effects and inference speed. The inference speed is increased by 22.2 times, mainly due to its use of native PyTorch implementation, which makes it cost-effective, low-latency, and high-throughput for the implementation of multi-turn dialogue inference scenarios.
However, StreamingLLM still has room for optimization. In this regard, the Colossal-AI team has launched SwiftInfer, which is based on TensorRT and can further improve the inference performance of large models by 46%, effectively solving the above problems.
The launch of StreamingLLM not only brings a new breakthrough to the open-source community but also provides a new possibility for the improvement of large model inference performance. In the future, with further optimization of SwiftInfer, we have reason to believe that the inference performance of large models will be faster, thus promoting the development of artificial intelligence technology.
【来源】https://mp.weixin.qq.com/s/fiYSESKcOgZIDe8dpLdAdQ
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