When an enterprise LLM retrieves a product name, technical specification, or standard contract clause, it's using expensive GPU computation designed for complex reasoning — just to access static information. This happens millions of times per day. Each lookup wastes cycles and inflates infrastructure costs. DeepSeek's newly released research on "conditional memory" addresses this architectural limitation directly. The work introduces Engram, a module that separates static pattern retrieval from dynamic reasoning. It delivers results that challenge assumptions about what memory is actually for in neural networks. The paper was co-authored by DeepSeek founder Liang Wenfeng.Through systematic experiments DeepSeek found the optimal balance between computation and memory [...]
The whale has resurfaced. DeepSeek, the Chinese AI startup offshoot of High-Flyer Capital Management quantitative analysis firm, became a near-overnight sensation globally in January 2025 with the rel [...]
Chinese artificial intelligence startup DeepSeek released two powerful new AI models on Sunday that the company claims match or exceed the capabilities of OpenAI's GPT-5 and Google's Gemini- [...]
DeepSeek’s announcement over the weekend that it has made its 75% price cut permanent on its flagship V4 Pro model is a disruptive assault on the capital-heavy business models of Silicon Valley’s [...]
DeepSeek, the Chinese artificial intelligence research company that has repeatedly challenged assumptions about AI development costs, has released a new model that fundamentally reimagines how large l [...]
DeepSeek continues to push the frontier of generative AI...in this case, in terms of affordability.The company has unveiled its latest experimental large language model (LLM), DeepSeek-V3.2-Exp, that [...]
Enterprises can't fix their GPU waste problem because the fix makes the problem worse. Releasing idle capacity would improve utilization, but the same shortage driving GPU prices up is exactly wh [...]
Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits.MeMo, a [...]
Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits.MeMo, a [...]