DeepSeek又一论文上新

Core Viewpoint - The DeepSeek team has released a new academic paper focusing on optimizing inference speed for large language models (LLMs), which is crucial for the practical application of AI agents [4][5]. Group 1: Research and Innovation - The paper, co-authored with Peking University and Tsinghua University, introduces an innovative inference system called DualPath, designed to enhance the performance of LLMs under agent workloads [4]. - The DualPath system employs a "dual-path reading KV-Cache" mechanism, redistributing storage network load, resulting in an offline inference throughput increase of 1.87 times and an average increase of 1.96 times in the number of agent operations per second for online services [4][5]. Group 2: Industry Context and Expectations - The introduction of DualPath addresses the significant changes in inference workloads as LLMs evolve from simple dialogue systems to complex agent systems capable of multi-turn interactions, which can reach dozens or even hundreds of rounds [4]. - There is a growing expectation for the release of DeepSeek's next flagship model, DeepSeek V4, with various rumors about its launch timeline ranging from early February to March [6]. - Recent leaks suggest that DeepSeek is testing a V4 Lite model, codenamed "Sealion-lite," which supports a context window of 1 million tokens and native multimodal inference [6]. Group 3: Market Reactions and Concerns - Despite the technical advancements presented in the paper, there is a sentiment in the industry that such optimizations are seen as a necessity due to GPU shortages, with some viewing it as "dirty work" rather than innovative [5]. - Concerns have been raised among investment institutions that the release of the new model could lead to significant market volatility, similar to the previous year's model launch [6].