Both at the tactical level and strategic level that paper means we will see _more_ nvidia gpus fly off the shelves. If anything it's showing why the h800 is a decent card and everyone in China should buy one (dozen thousands).
https://news.ycombinator.com/item?id=42856652
I have to agree. I'm not sure the architectural and engineering changes here don't put NVDA further ahead. Some stuff all gpu makers can do. Like quantize on transfer. But NVDA is so far ahead in other areas and DeepSeek leans heavily on that industry leading tech. At least at the low level.
But I'm betting the average investor doesn't have the level of facility with ML to properly analyze the information that's come out. Heck, I'm betting most of the people working in AI are really just TF or PyTorch monkeys. The real AI experts have probably been hovered up by MS or FB or AMZN or OpenAI or what have you. So it feels like we could get lucky here and the AI related market could drop like a rock taking the good with the bad. Which I think would be awesome for most people in a position to wait for it to happen.
What gets me about the deepseek work is that it's neither novel or theoretically difficult.
I've been using a similar synthetic data pipeline to generate examples for sota bert embedding models.
That low-hanging fruits like this are left unexplored by meta or alphabet - and if we're to be believed lead to crisis meetings for a week straight - makes one wonder wtf the engineers there are doing?
There's already some pretty impressive work being done with folks using just a pair of M2 Ultras with r1 in a "home lab" context that goes way beyond what you could previously do with llama.
There are things, like doc strings for functions, that llama models give better results for.
You can't pay me to use AMD cards. I have work to do. I don't have time to write drivers for cards that no one supports.
The point of the deepseek r1 paper is that the more synthetic data you generate by over trained models the better the resulting model. That means more GPUs. There is no reason why you can't train a dozen models like their nameless logic model in other domains and then use them for synthetic data augmentation.
So yes, an increase need for ever better thinking models.
On the inference side, why does one need Nvidia GPUs over Intel and AMD GPUs?
Not to mention there’s little real world evidence that AMD is better at inference speed not cost/inference over Nvidia.