The Ledger
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Lemkin: DeepSeek's AI model is intentionally crippled inside China
“And DeepSeek is intentionally crippled in China. It's not as good as it is here. It can't search the web and it is trained on different data as near as I can tell.”
Feldman says top Chinese open-source AI models lag closed-source models slightly
“This is made doubly worse by some of the best open source models were made by Chinese companies. And they are exceptionally good models. Kimi Ketu, Deep Seek. When the GLM, these are extraordinarily good models. They're not quite as good as the closed source m…”
Siddharth: Chinese open-source AI models like DeepSeek and Qwen are state-of-the-art
“I think it's very impressive, like the progress that they've made in open source with DeepSeek Kimi Ketu, Kuen. These models are state of the art.”
Ross: DeepSeek's breakthrough was an algorithmic gain in data generation
“There was an algorithmic improvement on that. And the algorithmic improvement, as I explained, you know, is this seemingly silly thing where they just wrote the answer in a box and then they knew what to look for rather than having to have a human being check …”
Ross: DeepSeek was created by a hedge fund, not the state directly
“Remember deep seek is a real, I mean, it's a hedge fund. They're doing this themselves and they're just influenced by the CCP”
Ross: OpenAI does not need to distill DeepSeek because OpenAI remains superior
“They don't need to because they're actually better still. They're a little bit better. So they could, but why would they?”
Ross: DeepSeek restricted signups due to a shortage of inference compute
“So, they ran out of compute, and this is why, this is the other reason why chip startups are gonna do just fine, because they ran out of inference compute.”
Ross: DeepSeek scraped OpenAI data while developing unique RL techniques
“And all of that said, they did a lot of really innovative things. So that's what makes it so complicated because on the one hand, they kind of just scraped the OpenAI model. On the other hand, they came up with some unique reinforcement learning techniques,”
Feldman: Running DeepSeek 671B on Standard SRAM Chips Requires Up to 8,000 Chips
“If you build a normal size chip with SRAM and you want to do a four hundred billion parameter model and inference, you might need 4000 chips. Or if you want to do a DeepSeq six 71, you might need six or 8000 chips.”
Ross: AI companies will use massive GPU clusters to generate synthetic data
“What you're going to see now is now that everyone has seen this deep seek architecture, they're going to go great. I have hundreds of thousands of GPUs. I'm now going to use a lot of them to create a lot of synthetic data. And then I'm going to train the bejes…”