大摩眼中的DeepSeek:以存代算、以少胜多
Seek .Seek .(US:SKLTY) 3 6 Ke·2026-01-22 09:09

Core Insights - DeepSeek is revolutionizing AI scalability by utilizing a hybrid architecture that replaces scarce high-bandwidth memory (HBM) with more cost-effective DRAM through an innovative module called "Engram" [1][3][5] Group 1: Engram Module and Conditional Memory - The Engram module introduces "Conditional Memory," separating static knowledge storage from dynamic reasoning, which significantly reduces reliance on expensive HBM [3][5] - This architecture allows for efficient retrieval of basic information without overloading HBM, thus freeing up capacity for more complex reasoning tasks [3][5] Group 2: Economic Impact on Infrastructure - The Engram architecture reshapes hardware cost structures by minimizing HBM dependency, potentially shifting infrastructure costs from GPUs to more affordable DRAM [5][6] - A 100 billion parameter Engram model requires approximately 200GB of system DRAM, indicating a 13% increase in the use of commodity DRAM per system [5][6] Group 3: Innovation Driven by Constraints - Despite limitations in advanced computing power and hardware access, Chinese AI models have rapidly closed the performance gap with global leaders, demonstrating "constraint-induced innovation" [6][7] - DeepSeek's advancements suggest that future AI capabilities may rely more on algorithmic and system-level innovations rather than merely increasing hardware resources [6][7] Group 4: Future Outlook - The upcoming DeepSeek V4 model is expected to achieve significant advancements in encoding and reasoning, potentially running on consumer-grade hardware like the RTX 5090 [7] - This development could lower the marginal costs of high-level AI inference, enabling broader deployment of AI applications without the need for expensive data center-grade GPU clusters [7]