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ZDNET's key takeaways
DeepSeek debuted Manifold-Constrained Hyper-Connections, or mHCs.
They offer a way to scale LLMs without incurring huge costs.
The company postponed the release of its R2 model in mid-2025.
Just before the start of the new year, the AI world was introduced to a potential game-changing new method for training advanced models.
A team of researchers from Chinese AI firm DeepSeek released a paper on Wednesday outlining what it called Manifold-Constrained Hyper-Connections, or mHC for short, which may provide a pathway for engineers to build and scale large language models without the huge computational costs that are typically required.
Also: Is DeepSeek's new model the latest blow to proprietary AI?
DeepSeek leapt into the cultural spotlight one year ago with its release of R1, a model that rivaled the capabilities of OpenAI's o1 and that was reportedly trained at a fraction of the cost. The release came as a shock to US-based tech developers, because it showed that access to huge reserves of capital and computing resources wasn't necessarily required to train cutting-edge AI models.
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