2025.02.24

Word count of this text: , estimated reading time is approximately minutes.

introduction：“The whale is making waves!（鲸鱼正在掀起波浪！）”有网友在DeepSeek的帖子下留言称。

Author: First Financial News, Liu Xiaojie

On [month] [day], the "Open Source Week" was launched, and the first code repository was made open source.

According to reports, this is an optimized high-efficiency decoding kernel specifically designed to handle variable-length sequences, which has now been put into production use. "It can achieve a memory bandwidth of / and computational performance on the platform," it was stated.

In simple terms, it is an optimization solution that enables large language models to run faster and more efficiently on platforms like this, particularly suitable for high-performance tasks. This code can accelerate the decoding process of large language models, thereby improving the model's response speed and throughput, which is especially important for real-time generation tasks such as chatbots, text generation, and more.

(-, multi-layer attention mechanism) is an improved attention mechanism designed to enhance the efficiency and performance of models when processing long sequences. Through parallel computation by multiple heads (), the model can simultaneously focus on information at different positions and semantic levels in the text, thereby more comprehensively and deeply capturing long-distance dependencies and complex semantic structures.

Previously, when analyzing the architecture, some practitioners mentioned that the essence of it is a lossy compression of (-, a caching mechanism) to enhance the storage of information. "This technology was first introduced in - and is currently the best method in open-source models to significantly reduce cache size."

What is the impact of open-sourcing this code? When asked this question, the response was that this code is like installing a "turbocharger" for the reasoning engine, enabling large models to handle complex tasks faster and more resource-efficiently, while also lowering the technical threshold. The significance is not just a technical optimization, but a crucial step towards breaking the monopoly on computing power and accelerating universal access.

Specifically, it can break through the computational bottleneck and reduce costs. Traditional decoding methods waste parallel computing capabilities when processing sequences of varying lengths (such as translating sentences of different lengths), akin to using a truck to transport small packages, where most of the space is unused. The improvement here is: through dynamic scheduling and memory optimization, the computational power (such as) is fully utilized, significantly increasing throughput under the same hardware conditions. This means that enterprises can accomplish the same tasks with fewer servers, directly lowering inference costs.

On the other hand, it can promote the practical application of large models. Variable-length sequences are the norm in real-world scenarios (such as chat conversations, document generation), but traditional methods require padding to a fixed length, leading to computational redundancy. Supporting dynamic processing of variable-length inputs allows applications (such as customer service robots, code generation) to respond faster and more smoothly, enhancing user experience and accelerating commercial implementation.

Previously, high-performance decoding kernels were predominantly monopolized by tech giants through closed-source means (such as optimization libraries), making it difficult for small and medium-sized enterprises and researchers to replicate. With the advent of open-source, developers can now freely access "industrial-grade optimization solutions," lowering the technical barriers and fostering the emergence of more innovative applications (such as small models in vertical fields).

" !（鲸鱼正在掀起波浪！）”有网友在的帖子下留言称。（注：的企业是鲸鱼）。

Some netizens also hope to open-source the code related to web search ( ), mentioning that " is truly (Open AI)."

This is just the beginning. It was announced last week that starting next week, several code repositories will be gradually open-sourced, "sharing our small but sincere progress in a completely transparent manner." It was stated that the fundamental building blocks of these online services have been documented, deployed, and tested in real-world production environments.

DeepSeek在公告中称自己是探索 AGI 的小公司，作为开源社区的一部分，每分享一行代码，都会成为加速AI行业发展的集体动力。同时，DeepSeek 称，没有高不可攀的象牙塔，只有纯粹的车库文化（不少美国著名企业从车库里诞生）和社区驱动的创新。 WeChat editor | 生产队的驴（拉磨中）

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马斯克赔特朗普万美元！

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