近日，一款名为StreamingLLM的开源方案在不到3个月的时间内，吸引了大量关注。该方案实现了多轮对话共400万个token，推理速度提升22.2倍，同时在不牺牲生成效果和推理速度的前提下，降低了推理成本。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。
为此，Colossal-AI团队开源了SwiftInfer，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。SwiftInfer的推出，无疑将为大模型推理性能带来显著提升，同时也有助于推动相关技术在实际应用中的发展。
英文翻译：
News Title: StreamingLLM reasoning performance upgraded, open-source solution attracts attention
Keywords: StreamingLLM, reasoning performance, open-source solution
News Content:
Recently, an open-source solution called StreamingLLM has attracted a large amount of attention. This solution achieves multi-round dialogue with a total of 4 million tokens, with a reasoning speed increase of 22.2 times, while reducing the reasoning cost without sacrificing generation effectiveness and reasoning speed. However, StreamingLLM uses native PyTorch implementation, and there is still room for optimization for the low-cost, low-latency, and high-throughput requirements of multi-round dialogue reasoning scenarios in practical applications.
To this end, the Colossal-AI team has opened up SwiftInfer, a StreamingLLM based on TensorRT, which can further improve the reasoning performance of large models by 46% and effectively solve the above problems. The introduction of SwiftInfer will undoubtedly bring significant improvements to the reasoning performance of large models and promote the development of related technology in practical applications.
【来源】https://mp.weixin.qq.com/s/fiYSESKcOgZIDe8dpLdAdQ
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