近日，一款名为StreamingLLM的开源方案在上线不到三个月的时间内，其GitHub项目标星达到了5.7k star，受到了广泛关注。据悉，该方案能够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，推理速度提升了22.2倍。这一成果被认为是MIT的重要突破，为大规模语言模型提供了新的思路。
StreamingLLM项目使用原生PyTorch实现，虽然在多轮对话推理场景中取得了显著的效果，但对于低成本、低延迟、高吞吐等需求仍有优化空间。为此，Colossal-AI团队推出了SwiftInfer，这是一个基于TensorRT的StreamingLLM解决方案，可以进一步提升大模型推理性能46%，有效解决上述问题。
这一创新技术为人工智能领域带来了新的机遇，有望在自然语言处理、对话系统、推荐系统等领域发挥重要作用。未来，随着技术的不断发展和优化，相信会有更多的企业和研究机构加入到这一领域的研究中来，共同推动人工智能技术的发展。
英文翻译：
Title: Open Source StreamingLLM Achieves Reasoning Acceleration and Cost Reduction
Keywords: Open Source, Reasoning Acceleration, Cost Reduction
News content:
Recently, an open-source solution called StreamingLLM has gained widespread attention, with its GitHub project reaching 5.7k stars in less than three months since its launch. Reportedly, this solution can facilitate multi-turn dialogue of up to 4 million tokens with a 22.2-fold acceleration in reasoning speed without compromising generation quality or speed. This is considered a significant breakthrough of MIT, providing a new approach for large-scale language models.
The StreamingLLM project is implemented using native PyTorch, which has achieved remarkable results in multi-turn dialogue reasoning scenarios. However, there is still room for optimization in terms of low cost, low latency, and high throughput. In response, the Colossal-AI team has introduced SwiftInfer, a StreamingLLM-based solution on TensorRT, which can further enhance the reasoning performance of large models by 46%, effectively addressing the aforementioned issues.
This innovative technology brings new opportunities to the field of artificial intelligence and is expected to play a significant role in natural language processing, dialogue systems, recommendation systems, and more. With the continuous development and optimization of this technology, more enterprises and research institutions are expected to join this field, collectively driving the advancement of artificial intelligence.
【来源】https://mp.weixin.qq.com/s/fiYSESKcOgZIDe8dpLdAdQ
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