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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

#201

Post by startupsfail »

skizm wrote: post #192

I'm wondering if there's a (probably illegal) strategy in the making here: - Wait till NVDA rebounds in price. - Create an OpenAI "competitor" that is powered by Llama or a similar open weights model. - Obscure the fact that the company runs on this open tech and make it seem like you've developed your own models, but don't outright lie. - Release an app and whitepaper (whitepaper looks and sounds technical, but is i…

- Fail at the above.

I don’t think this is what happened with DeepSeek. It seems that they’ve genuinely optimized their model for efficiency and used GPUs properly (tiled FP8 trick and FP8 training). And came out on top.

The impact on the NVIDIA stock is ridiculous. DeepSeek took the advantage of flexible GPU architecture (unlike inflexible hardware acceleration).

Re: The impact of competition and DeepSeek on Nvidia

#203

Post by onlyrealcuzzo »

tomrod wrote: post #158
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…

Conversely, how much larger can you scale if frontier models only currently need 3 consumer computers? Imagine having 300. Could you build even better models? Is DeepSeek the right team to deliver that, or can OpenAI, Meta, HF, etc. adapt? Going to be an interesting few months on the market. I think OpenAI lost a LOT in the board fiasco. I am bullish on HF. I anticipate Meta will lose folks to brain drain in response…

If you watch this video, it explains well what the major difference is between DeepSeek and existing LLMs: https://www.youtube.com/watch?v=DCqqCLlsIBU

It seems like there is MUCH to gain by migrating to this approach - and it theoretically should not cost more to switch to that approach than vs the rewards to reap.

I expect all the major players are already working full-steam to incorporate this into their stacks as quickly as possible.

IMO, this seems incredibly bad to Nvidia, and incredibly good to everyone else.

I don't think this seems particularly bad for ChatGPT. They've built a strong brand. This should just help them reduce - by far - one of their largest expenses.

They'll have a slight disadvantage to say Google - who can much more easily switch from GPU to CPU. ChatGPT could have some growing pains there. Google would not.

Re: The impact of competition and DeepSeek on Nvidia

#204

Post by accra4rx »

pjdesno wrote: post #176

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-q…

Love those analogies . This is one of main reason I love hacker news / reddit . Honest golden experiences

Re: The impact of competition and DeepSeek on Nvidia

#205

Post by cosmic_cheese »

idonotknowwhy wrote: post #126
csomar wrote: post #119

Earlier quoted context omitted.

> - Better Linux drivers than AMD Unless something radically changed in the last couple years, I am not sure where you got this from? (I am specifically talking about GPUs for computer usage rather than training/inference)

> Unless something radically changed in the last couple years, I am not sure where you got this from? This was the first thing that stuck out to me when I skimmed the article, and the reason I decided to invest the time reading it all. I can tell the author knows his shit and isn't just parroting everyone's praise for AMD Linux drivers. > (I am specifically talking about GPUs for computer usage rather than training/i…

Another ding against Nvidia for Linux desktop use is that only some distributions either make it easy to install and keep the proprietary drivers updated (e.g. Ubuntu) and/or ship variants with the proprietary drivers preinstalled (Mint, Pop!_OS, etc).

This isn’t a barrier for Linux veterans but it adds significant resistance for part-time users, even those that are technically inclined, compared to the “it just works” experience one gets with an Intel/AMD GPU under just about every Linux distro.

Re: The impact of competition and DeepSeek on Nvidia

#206

Post by [deleted] »

startupsfail wrote: post #201
skizm wrote: post #192

I'm wondering if there's a (probably illegal) strategy in the making here: - Wait till NVDA rebounds in price. - Create an OpenAI "competitor" that is powered by Llama or a similar open weights model. - Obscure the fact that the company runs on this open tech and make it seem like you've developed your own models, but don't outright lie. - Release an app and whitepaper (whitepaper looks and sounds technical, but is i…

- Fail at the above. I don’t think this is what happened with DeepSeek. It seems that they’ve genuinely optimized their model for efficiency and used GPUs properly (tiled FP8 trick and FP8 training). And came out on top. The impact on the NVIDIA stock is ridiculous. DeepSeek took the advantage of flexible GPU architecture (unlike inflexible hardware acceleration).

[deleted]

Re: The impact of competition and DeepSeek on Nvidia

#207

Post by tw1984 »

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…

> If most of NVIDIAs moat is in being able to efficiently interconnect thousands of GPUs

nah. it moat is CUDA and millions of devs using CUDA aka the ecosystem

Re: The impact of competition and DeepSeek on Nvidia

#208

Post by vonneumannstan »

btbuildem wrote: post #199

I always appreciate reading a take from someone who's well versed in the domains they have opinions about. I think longer-term we'll eat up any slack in efficiency by throwing more inference demands at it -- but the shift is tectonic. It's a cultural thing. People got acclimated to shlepping around morbidly obese node packages and stringing together enormous python libraries - meanwhile the deepseek guys out here car…

This is a bizarre take. First Deepseek no doubt is still using the same bloated Python ML packages as everyone else. Second since this is "open source" it's pretty clear that the big labs are just going to replicate this basically immediately and with their already massive compute advantages put models out that are extra OOM larger/better/etc/ than what Deepseek can possibly put out. Theres just no reason to think that e.g. a 10x increase in training efficiency does anything but increase the size of the next model generation by 10x.

Re: The impact of competition and DeepSeek on Nvidia

#209

Post by AlanYx »

pjdesno wrote: post #176

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-q…

Another aspect that reinforces your point is that the ATM push (and subsequent downfall) was not just bandwidth-motivated but also motivated by a belief that ATM's QoS guarantees were necessary. But it turned out that software improvements, notably MPLS to handle QoS, were all that was needed.

Re: The impact of competition and DeepSeek on Nvidia

#210

Post by qwertox »

Considering the fact that current models were trained on top-notch books, those read and studied by the most brilliant engineers, the models are pretty dumb.

They are more like the thing which enabled computers to work with and digest text instead of just code. The fact that they can parrot pretty interesting relationships from the texts they've consumed kind of proofs that they are capable of statistically "understanding" what we're trying to talk with them about, so it's a pretty good interface.

But going back to the really valuable content of the books they've been trained on, they just don't understand it. There's other AI which needs to get created which can really learn the concepts taught in those books instead of just the words and the value of the proximities between them.

To learn that other missing part will require hardware just as uniquely powerful and flexible as what Nvidia has to offer. Those companies now optimizing for inference and LLM training will be good at it and have their market share, but they need to ensure that their entire stack is as capable of Nvidia's stack, if they also want to be part of future developments. I don't know if Tenstorrent or Groq are capable of doing this, but I doubt it.

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