大型语言模型 (LLM) 在 Transformer 架构上构建，最近取得了重要的技术里程碑。这些模型在理解和生成类似人类的文本方面的非凡能力，对各种人工智能 (AI) 应用产生了重大影响。尽管这些模型运行良好，但在低资源环境中成功部署它们存在许多挑战。该行业对此问题给予了高度关注，特别是在访问 GPU 硬件资源受限的情况下。在这些情况下，基于 CPU 的替代方案变得至关重要。 提高推理性能对于降低成本和克服稀缺硬件资源的局限性至关重要。在最近的一项研究中，一组研究人员提出了一种易于部署的方法，可以提高 LLM 在 CPU 上的推理性能。该解决方案的主要特点之一是实施了一种实用的方法来降低 KV 缓存大小，而不会牺牲准确性。为了确保 LLM 即使在资源有限的情况下也能正常运行，这种优化至关重要。

🤔 **改进的推理性能：** 该研究提出了一个易于部署的框架，用于优化 LLM 在 CPU 上的推理性能。该框架包含多个关键优化技术，例如 SlimAttention，以及降低 KV 缓存大小的策略，这些策略通过减少内存使用量来提高效率，同时保持准确性。

🤝 **分布式推理优化：** 研究人员还开发了一种分布式推理优化方法，利用 oneAPI 集体通信库。这种方法通过在多个 CPU 之间实现高效的通信和处理，显著提高了 LLM 的可扩展性和性能，使其能够有效地处理大型任务。

🎯 **模型特定优化：** 该研究还包含针对流行模型（如 Qwen、Llama、ChatGLM、Baichuan 和 Opt 系列）的特定优化。这些优化针对不同的 LLM 过程和层进行定制，确保解决方案的灵活性，使其适用于各种 LLM。

🚀 **实际应用：** 研究人员的目标是将这些优化应用于实践，以加速 LLM 在 CPU 上的运行，从而提高它们在低资源环境中的部署成本效益和可访问性。

📚 **研究成果：** 该研究的贡献包括开发用于 CPU 上 LLM 优化的独特方法、提出降低 KV 缓存大小而不会牺牲准确性的实用策略，以及开发专门针对 CPU 上 LLM 的分布式推理优化方法。

💡 **代码和资源：** 研究人员提供了相关的代码和资源，包括论文和 GitHub 存储库，以便其他研究人员和开发者可以利用这些优化技术。

🚀 **未来展望：** 该研究为在 CPU 上优化 LLM 推理性能提供了有价值的见解，为在资源受限环境中有效部署 LLM 开辟了新的可能性。未来，该领域有望进一步发展，以实现更快的推理速度、更高的效率和更广泛的 LLM 可用性。

Large Language Models (LLMs) built on the Transformer architecture have recently attained important technological milestones. The remarkable skills of these models in comprehending and producing writing that resembles that of a human have had a significant impact on a variety of Artificial Intelligence (AI) applications. Although these models function admirably, there are many obstacles to successfully implementing them in low-resource contexts. The industry has given this problem a lot of attention, particularly in situations when access to GPU hardware resources is constrained. In these kinds of situations, CPU-based alternatives become essential.

Improving inference performance is crucial to reducing costs and getting past the limitations of scarce hardware resources. In a recent research, a team of researchers has presented an easy-to-deploy approach that improves the inference performance of LLMs on CPUs. This solution’s implementation of a practical way to lower the KV cache size without sacrificing accuracy is one of its main features. In order to guarantee that LLMs can operate well even with limited resources, this optimization is essential.

The study has also suggested a technique for distributed inference optimization that makes use of the oneAPI Collective Communications Library. By facilitating effective communication and processing among numerous CPUs, this method greatly improves the scalability and performance of LLMs. Moreover, tailored optimizations for the most popular models are covered, guaranteeing that the solution is flexible and suitable for a variety of LLMs. The goal of putting these optimizations into practice is to speed up LLMs on CPUs, which will increase their affordability and accessibility for deployment in low-resource settings. 

The team has summarized their primary contributions as follows.

The team has provided unique LLM optimization methods on CPUs, such as SlimAttention. These methods are compatible with popular models such as Qwen, Llama, ChatGLM, Baichuan, and the Opt series and feature distinct optimizations for LLM procedures and layers.

A workable strategy has been suggested to reduce the KV cache size without sacrificing accuracy. This method improves memory efficiency without appreciably degrading the output quality of the model.

Specifically for LLMs on CPUs, the team has developed a distributed inference optimization approach. This method is suitable for large-scale applications since it guarantees scalability and effective low-latency inference.

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