近日，一款名为StreamingLLM的大模型开源方案在GitHub上引起了广泛关注。该方案凭借其创新的设计理念，实现了在不牺牲生成效果、推理速度的前提下，多轮对话共400万个token，推理速度提升22.2倍的成果。这一突破性技术，使得大模型在实际应用中的推理成本得到大幅降低。
然而，StreamingLLM在使用原生PyTorch实现的过程中，对于多轮对话推理场景的低成本、低延迟、高吞吐等需求，仍有优化空间。为此，Colossal-AI团队开源了基于TensorRT的SwiftInfer，进一步提升大模型推理性能46%，有效解决了上述问题。
SwiftInfer作为一款基于TensorRT的StreamingLLM，其卓越的推理性能和低成本、低延迟、高吞吐等特点，使其在多轮对话推理场景中具有极高的应用价值。这款开源方案的推出，无疑将为大模型在实际应用中的普及和发展带来极大的推动力。
英文翻译：
News Title: StreamingLLM Reasoning Performance Boosted, Open-source Solution Breaks Cost Barrier
Keywords: StreamingLLM, Reasoning Performance, Open-source Solution
News Content:
Recently, a large-model open-source solution named StreamingLLM has attracted widespread attention on GitHub. With its innovative design concept, StreamingLLM enables multi-round dialogue with up to 4 million tokens and a reasoning speed increase of 22.2 times without sacrificing generation effectiveness and reasoning speed. This groundbreaking technology significantly reduces the reasoning cost in practical applications.
However, during the implementation of StreamingLLM using native PyTorch, there is still room for optimization in terms of low-cost, low-latency, and high-throughput requirements for multi-round dialogue reasoning scenarios. To address this issue, the Colossal-AI team has open-sourced SwiftInfer, a StreamingLLM based on TensorRT, which further improves large-model reasoning performance by 46% and effectively solves the above problems.
As a StreamingLLM based on TensorRT, SwiftInfer’s excellent reasoning performance, low cost, low latency, and high throughput make it highly applicable in multi-round dialogue reasoning scenarios. The introduction of this open-source solution will undoubtedly provide a significant boost to the popularization and development of large models in practical applications.
【来源】https://mp.weixin.qq.com/s/fiYSESKcOgZIDe8dpLdAdQ
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