link seems to be dead... is this article still up somewhere?
It's back up, but just in case:
The impact of competition and DeepSeek on Nvidia
21–30 of 500 posts
Re: The impact of competition and DeepSeek on Nvidia
#22[deleted]
Re: The impact of competition and DeepSeek on Nvidia
#23This was an amazing summary of the landscape of ML currently. I think the title does the article injustice, or maybe it’s too long for people to read to appreciate it (eg the deepseek stuff can be an article within itself). Whatever the ones with longer attention span will benefit from this read. Thanks for summarising this up!
The site is currently offline, here's a snapshot:
Re: The impact of competition and DeepSeek on Nvidia
#24Sorry, my blog crashed! Had a stupid bug where it was calling GitHub too frequently to pull in updated markdown for the posts and kept getting rate limits. Had to rewrite it but it should be much better now.
Re: The impact of competition and DeepSeek on Nvidia
#25It seems like a pointless discussion since DeepSeek uses Nvidia GPUs after all.
Yep some CEO said they have 50K GPUs of the prior generation. They probably accumulated them through intermediaries that are basically helping nvidia sell to sanctioned parties by proxy
Re: The impact of competition and DeepSeek on Nvidia
#26Great article but it seems to have a fatal flaw.
As pointed out in the article, Nvidia has several advantages including:
- Better Linux drivers than AMD
- CUDA
- pytorch is optimized for Nvidia
- High-speed interconnect
- George Hotz is making better drivers for AMD
- MLX, Triton, JAX: Higher level abstractions that compile down to CUDA
- Cerbras and Groq solve the interconnect problem
Re: The impact of competition and DeepSeek on Nvidia
#27> While Apple's focus seems somewhat orthogonal to these other players in terms of its mobile-first, consumer oriented, "edge compute" focus, if it ends up spending enough money on its new contract with OpenAI to provide AI services to iPhone users, you have to imagine that they have teams looking into making their own custom silicon for inference/training
This is already happening today. Most of the new LLM features announced this year are primarily on-device, using the Neural Engine, and the rest is in Private Cloud Compute, which is also using Apple-trained models, on Apple hardware.
The only features using OpenAI for inference are the ones that announce the content came from ChatGPT.
Re: The impact of competition and DeepSeek on Nvidia
#28Yesterday I wrote up all my thoughts on whether NVDA stock is finally a decent short (or at least not a good thing to own at this point). I’m a huge bull when it comes to the power and potential of AI, but there are just too many forces arrayed against them to sustain supernormal profits. Anyway, I hope people here find it interesting to read, and I welcome any debate or discussion about my arguments.
Good article. Maybe I missed it, but I see lots of analysis without a clear concluding opinion.
Re: The impact of competition and DeepSeek on Nvidia
#29> While Apple's focus seems somewhat orthogonal to these other players in terms of its mobile-first, consumer oriented, "edge compute" focus, if it ends up spending enough money on its new contract with OpenAI to provide AI services to iPhone users, you have to imagine that they have teams looking into making their own custom silicon for inference/training This is already happening today. Most of the new LLM features…
"if it ends up spending enough money on its new contract with OpenAI to provide AI services to iPhone users"
John Gruber says neither Apple nor OpenAI are paying for that deal: https://daringfireball.net/linked/2024/06/13/gurman-openai-a...