DeepSeek’s recently released open-source reasoning, large language model (LLM) V3-R1 has benchmarked closely to OpenAI’s o1 framework. The Chinese company drastically lowered the cost of running its LLM via several novel methods. Firstly, DeepSeek drastically improved reasoning using stronger reinforcement learning techniques that reduce precision while maintaining accuracy. They also employ a mixture-of-experts architecture, which combines many focused models that only call on individual components when necessary, rather than relying on one large model that requires vast amounts of memory. These techniques allowed for high process performance with limited hardware capabilities.
On top of achieving similar performance to OpenAI’s o1, DeepSeek R1 is believed to have been trained on chips comparable to Nvidia’s A100 chip, which is two generations behind the latest Blackwell chip. This drastic change in model optimization has shifted industry leaders’ focus to refining their models to improve efficiency and reduce power consumption. We do not expect this to change near-term data center growth, but it raises questions about longer-term capital allocations for computing hub expansion.
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