DeepSeek CEO Liang Wenfeng told investors that the company is training a 2 trillion-parameter model and plans to develop an 8 trillion-parameter model afterward. The company’s current flagship, V4-Pro, has 1.6 trillion total parameters and has been open-sourced with 49 billion activated parameters disclosed. Among ultra-large models with publicly available weights, Moonshot AI’s Kimi K3 stands at 2.8 trillion parameters. Kimi K3 uses a mixture-of-experts, or MoE, architecture, with 104 billion parameters activated per token out of its 2.8 trillion total. On that basis, DeepSeek’s planned 8 trillion-parameter model would be close to nearly three times that scale. DeepSeek also completed a fundraising round of more than 50 billion yuan in July, reaching a valuation above $50 billion, according to ChainCatcher.
DeepSeek CEO Liang Wenfeng told investors that the company is training a 2 trillion-parameter model and plans to build an 8 trillion-parameter model after that, according to ChainCatcher.
DeepSeek’s current flagship model, V4-Pro, has 1.6 trillion total parameters. The model has been open-sourced, with 49 billion activated parameters disclosed.
Comparison with publicly weighted large models
Among ultra-large models with publicly available weights, Moonshot AI’s Kimi K3 has reached 2.8 trillion parameters. Kimi K3 uses a mixture-of-experts (MoE) architecture, with 104 billion parameters activated per token out of its 2.8 trillion total parameters.
By that comparison, DeepSeek’s planned 8 trillion-parameter model would be close to nearly three times the scale of Kimi K3.
Funding and valuation
DeepSeek completed a fundraising round of more than 50 billion yuan in July, at a valuation above $50 billion.
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