近日，Colossal-AI团队开源了一款基于TensorRT的SwiftInfer,可以进一步提升大模型推理性能46%,有效解决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。该项目在上线不到3个月时间内，GitHub项目标星达到5.7k star。
据悉，StreamingLLM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。这一突破性的进展将为自然语言处理领域带来重大影响。
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。因此，Colossal-AI团队推出了SwiftInfer,基于TensorRT的StreamingLLM,进一步优化了大模型推理性能。
SwiftInfer是基于TensorRT的高性能推理引擎，可以大幅提高模型推理速度和效率。它采用了多种技术手段，如模型并行、数据并行等，以实现更高的吞吐量和更低的延迟。同时，SwiftInfer还支持动态调整模型结构和参数，以适应不同的场景需求。
目前，SwiftInfer已经在多个自然语言处理任务中取得了显著的成果。例如，在机器翻译任务中，SwiftInfer的性能比原始模型提高了近30%;在文本分类任务中，性能提高了近40%。这些成果表明，SwiftInfer具有很大的潜力和前景。
总之，Colossal-AI团队开源的SwiftInfer为自然语言处理领域带来了新的希望。它不仅可以进一步提高大模型推理性能，还可以为多轮对话推理场景落地应用提供更好的解决方案。未来，随着技术的不断发展和完善，我们有理由相信自然语言处理领域将会迎来更加美好的明天。
英语如下：
Title: “StreamingLLM Open Source Solution Upgraded, Inference Speed Increases by 46%, Helping Large Model Applications”
Keywords: 4 million tokens, increased inference speed, open source solution, cost reduction, SwiftInfer, large model inference
Recently, the Colossal-AI team released an open source solution based on TensorRT called SwiftInfer, which can further increase the inference performance of large models by 46%. This effectively solves the low cost, low latency, and high throughput requirements of multi-round dialogue inference scenarios. The project has gained more than 57,000 stars on GitHub in less than three months.
It is reported that StreamingLLM can achieve multi-round dialogue with a total of 4 million tokens without sacrificing generation effects or inference speed. This breakthrough progress will have a significant impact on the field of natural language processing.
However, StreamingLLM is implemented using native PyTorch, leaving room for improvement in terms of low cost, low latency, and high throughput requirements for multi-round dialogue inference scenarios. Therefore, the Colossal-AI team launched SwiftInfer, a streaming version of StreamingLLM based on TensorRT, to further optimize the large model inference performance.
SwiftInfer is a high-performance inference engine based on TensorRT, which can significantly improve the speed and efficiency of model inference. It adopts various techniques such as model parallelism and data parallelism to achieve higher throughput and lower latency. Additionally, SwiftInfer supports dynamic adjustment of model structure and parameters to adapt to different scene requirements.
Currently, SwiftInfer has achieved significant results in several natural language processing tasks. For example, in machine translation tasks, SwiftInfer’s performance is nearly 30% higher than that of the original model; in text classification tasks, performance is improved by nearly 40%. These results demonstrate the great potential and prospects of SwiftInfer.
In summary, the open source SwiftInfer solution developed by Colossal-AI brings new hope to the field of natural language processing. It can not only further improve the large model inference performance but also provide better solutions for multi-round dialogue inference scenarios. In the future, with the continuous development and improvement of technology, we have reason to believe that the field of natural language processing will usher in a better tomorrow.
【来源】https://mp.weixin.qq.com/s/fiYSESKcOgZIDe8dpLdAdQ
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