Nostr News Network on Nostr: DeepSeek founder Liang Wenfeng: >> Studies machine vision at Zhejiang University >> ...
DeepSeek founder Liang Wenfeng:
>> Studies machine vision at Zhejiang University
>> At 30 in 2015, launches High-Flyer quant hedge fund
>> Makes a fortune (now $8B AUM)
>> Wants to build “human” level AI as side hustle and pitches partners but they initially sceptical
>> Buys 10,000 H800 chips in 2021 and brings over his top hedge fund employees (all have tons of experience squeezing juice out of Nvidia GPUs for the fund)
>> Launched DeepSeek in 2023 and hires dozens of PhDs from top Chinese universities (Peking, Tsinghua and Beihang)
>> Pays top top top salary for tech talent only matched by Bytedance in China…wants DeepSeek to be leading “local” company
>> US export restrictions force DeepSeek team to get creative and they do, finding new training methods to make LLM models (V3, r1) competitive with OpenAI, Anthropic, Gemini, Grok, LLama etc at ~1/20th the cost
>> Training costs not exactly apples-to-apples but novel methods and clear improvements in efficiency (also questions around copying other models, larger H-100 clusters they maybe can’t talk about and/or CCP support)
>> Open sources and publishes methods (r1 reasoning paper has 200+ authors)
>> DeepSeek just hit top of App Store
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>> Studies machine vision at Zhejiang University
>> At 30 in 2015, launches High-Flyer quant hedge fund
>> Makes a fortune (now $8B AUM)
>> Wants to build “human” level AI as side hustle and pitches partners but they initially sceptical
>> Buys 10,000 H800 chips in 2021 and brings over his top hedge fund employees (all have tons of experience squeezing juice out of Nvidia GPUs for the fund)
>> Launched DeepSeek in 2023 and hires dozens of PhDs from top Chinese universities (Peking, Tsinghua and Beihang)
>> Pays top top top salary for tech talent only matched by Bytedance in China…wants DeepSeek to be leading “local” company
>> US export restrictions force DeepSeek team to get creative and they do, finding new training methods to make LLM models (V3, r1) competitive with OpenAI, Anthropic, Gemini, Grok, LLama etc at ~1/20th the cost
>> Training costs not exactly apples-to-apples but novel methods and clear improvements in efficiency (also questions around copying other models, larger H-100 clusters they maybe can’t talk about and/or CCP support)
>> Open sources and publishes methods (r1 reasoning paper has 200+ authors)
>> DeepSeek just hit top of App Store
***