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The impact of competition and DeepSeek on Nvidia

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Re: The impact of competition and DeepSeek on Nvidia

#171

Post by neuronic »

brandonpelfrey wrote: post #157

Great article. I still feel like very few people are viewing the Deepseek effects in the right light. If we are 10x more efficient it's not that we use 1/10th the resources we did before, we expand to have 10x the usage we did before. All technology products have moved this direction. Where there is capacity, we will use it. This argument would not work if we were close to AGI or something and didn't need more, but I…

Would this not mean we need much much more training data to fully utilize the now "free" capacities?

Re: The impact of competition and DeepSeek on Nvidia

#172

Post by jnwatson »

brandonpelfrey wrote: post #157

Great article. I still feel like very few people are viewing the Deepseek effects in the right light. If we are 10x more efficient it's not that we use 1/10th the resources we did before, we expand to have 10x the usage we did before. All technology products have moved this direction. Where there is capacity, we will use it. This argument would not work if we were close to AGI or something and didn't need more, but I…

This is called Jevons Paradox.

https://en.wikipedia.org/wiki/Jevons_paradox.

Re: The impact of competition and DeepSeek on Nvidia

#173

Post by mikestew »

neuronic wrote: post #166
colinnordin wrote: post #151

Great article. > Now, you still want to train the best model you can by cleverly leveraging as much compute as you can and as many trillion tokens of high quality training data as possible, but that's just the beginning of the story in this new world; now, you could easily use incredibly huge amounts of compute just to do inference from these models at a very high level of confidence or when trying to solve extremely…

> NVIDIAs moat Offtopic, but your comment finally pushed me over the edge to semantic satiation [1] regarding the word "moat". It is incredible how this word turned up a short while ago and now it seems to be a key ingredient of every second comment. [1] https://en.wikipedia.org/wiki/Semantic_satiation

It is incredible how this word turned up a short while ago…

I’m sure if I looked, I could find quotes from Warren Buffet (the recognized originator of the term) going back a few decades. But your point stands.

Re: The impact of competition and DeepSeek on Nvidia

#174

Post by liuliu »

This is a humble and informed acrticle (comparing to others written by financial analysts the past a few days). But still have the flaw of over-estimating efficiency of deploying a 687B MoE model on commodity hardware (to use locally, cloud providers will do efficient batching and it is different): you cannot do that on any single Apple hardware (need to at least hook up 2 M2 Ultra). You can barely deploy that on desktop computers just because non-register DDR5 can have 64GiB per stick (so you are safe with 512 RAM). Now coming to PCIe bandwidth: 37B per token activation means exactly that, each activation requires new set of 37B weights, so you need to transfer 18GiB per token into VRAM (assuming 4-bit quant). PCIe 5 (5090) have 64GB/s transfer speed so your upper bound is limited to 4 tok/s with a well balanced propose built PC (and custom software). For programming tasks that usually requires ~3000 tokens for thinking, we are looking at 12 mins per interaction.

Re: The impact of competition and DeepSeek on Nvidia

#175

Post by chpatrick »

miraculixx wrote: post #88

If we are to get to AGI why do we need to train on all data? That's silly, and all we get is compression and probabliatic retrieval. Intelligence by definition is not compression, but ability to think and act according to new data, based on experience. Trully AGI models will work on the this principle, not on best compression of as much data as possible. We need a new approach.

“If you can't explain it to a six year old, you don't understand it yourself.” -> "If you can compress knowledge, you understand it."

Re: The impact of competition and DeepSeek on Nvidia

#176

Post by pjdesno »

The description of DeepSeek reminds me of my experience in networking in the late 80s - early 90s.

Back then a really big motivator for Asynchronous Transfer Mode (ATM) and fiber-to-the-home was the promise of video on demand, which was a huge market in comparison to the Internet of the day. Just about all the work in this area ignored the potential of advanced video coding algorithms, and assumed that broadcast TV-quality video would require about 50x more bandwidth than today's SD Netflix videos, and 6x more than 4K.

What made video on the Internet possible wasn't a faster Internet, although the 10-20x increase every decade certainly helped - it was smarter algorithms that used orders of magnitude less bandwidth. In the case of AI, GPUs keep getting faster, but it's going to take a hell of a long time to achieve a 10x improvement in performance per cm^2 of silicon. Vastly improved training/inference algorithms may or may not be possible (DeepSeek seems to indicate the answer is "may") but there's no physical limit preventing them from being discovered, and the disruption when someone invents a new algorithm can be nearly immediate.

Re: The impact of competition and DeepSeek on Nvidia

#178

Post by typeofhuman »

1970-01-01 wrote: post #177
typeofhuman wrote: post #170

I'm rooting for DeepSeek (or any competitor) against OpenAI because I don't like Sam Altman. I'm confident in admitting it.

The enemy of your enemy is only temporarily your friend.

Wise words from the epoch of time.

Re: The impact of competition and DeepSeek on Nvidia

#179

Post by esafak »

p0w3n3d wrote: post #165

> which require low-latency responses, such as content moderation, fraud detection, dynamic pricing , etc. Is it even legal to give different prices to different customers?

It depends on what basis. You can't discriminate based on protected classes.
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