[LrnByTeach]

Losers & Winners from Llama-3-400B Matching 'Claude 3 Opus' etc..

Losers:

- Nvidia Stock : lid on GPU growth in the coming year or two as "Nation states" use Llama-3/Llama-4 instead spending $$$ on GPU for own models, same goes with big corporations.

- OpenAI & Sam: hard to raise speculated $100 Billion, Given GPT-4/GPT-5 advances are visible now.

- Google : diminished AI superiority posture

Winners:

- AMD, intel: these companies can focus on Chips for AI Inference instead of falling behind Nvidia Training Superior GPUs

- Universities & rest of the world : can work on top of Llama-3

[vineyardmike]

I also disagree on Google...

Google's business is largely not predicated on AI the way everyone else is. Sure they hope it's a driver of growth, but if the entire LLM industry disappeared, they'd be fine. Google doesn't need AI "Superiority", they need "good enough" to prevent the masses from product switching.

If the entire world is saturated in AI, then it no longer becomes a differentiator to drive switching. And maybe the arms race will die down, and they can save on costs trying to out-gun everyone else.

[cm2012]

AI is taking marketshare from search slowly. More and more people will go to the AI to find things and not a search bar. It will be a crisis for Google in 5-10 years.

[mark_l_watson]

I think I agree with you. I signed up for Perplexity Pro ($20/month) many months ago thinking I would experiment with it a month and cancel. Even though I only make about a dozen interactions a week, I can’t imagine not having it available.

That said, Google’s Gemini integration with Google Workplace apps is useful right now, and seems to be getting better. For some strange reason Google does not have Gemini integration with Google Calendar and asking the GMail integration what is on my schedule is only accurate if information is in emails.

I don’t intend to dump on Google, I liked working there and I use their paid for products like GCP, YouTube Plus, etc., but I don’t use their search all that often. I am paying for their $20/month LLM+Google One bundle, and I hope that evolves into a paid for high quality, no ad service.

[zingelshuher]

Only if it does nothing. In fact Google is one of the major players in LLM field. The winner is hard to predict, chip makers likely ;) Everybody jumped on bandwagon, Amazon is jumping...

[endisneigh]

Source?

[exoverito]

Anecdotally speaking I use google search much less frequently and instead opt for GPT4. This is also what a number of my colleagues are doing as well.

[zingelshuher]

I often use ChatGPT4 for technical info. It's easier then scrolling through pages whet it works. But.. the accuracy is inconsistent, to put it mildly. Sometimes it gets stuck on wrong idea.

Interesting how far LLMs can get? Looks like we are close to scale-up limit. It's technically difficult to get bigger models. The way to go probably is to add assisting sub-modules. Examples would be web search, have it already. Database of facts, similar to search. Compilers, image analyzers, etc. With this approach LLM is only responsible for generic decisions and doesn't need to be that big. No need to memorize all data. Even logic can be partially outsourced to sub-module.

[cm2012]

I expect a 5x improvement before EOY, I think GPT5 will come out.

[LrnByTeach]

my own analysis

[season2episode3]

Google’s play is not really in AI imo, it’s in the the fact that their custom silicon allows them to run models cheaply.

Models are pretty much fungible at this point if you’re not trying to do any LoRAs or fine tunes.

[int_19h]

There's still no other model on par with GPT-4. Not even close.

[herewego]

Many disagree. “Not even close” is a strong position to take on this.

[int_19h]

It takes less than an hour of conversation with either, giving them a few tasks requiring logical reasoning, to arrive at that conclusion. If that is a strong position, it's only because so many people seem to be buying the common scoreboards wholesale.

[herewego]

That’s very subjective and case dependent. I use local models most often myself with great utility and advocate for giving my companies the choice of using either local models or commercial services/APIs (ChatGPT, GPT-4 API, some Llama derivative, etc.) based on preference. I do not personally find there to be a large gap between the capabilities of commercial models and the fine-tuned 70b or Mixtral models. On the whole, individuals in my companies are mixed in their opinions enough for there to not be any clear consensus on which model/API is best objectively — seems highly preference and task based. This is anecdotal (though the population size is not small), but I think qualitative anec-data is the best we have to judge comparatively for now.

I agree scoreboards are not a highly accurate ranking of model capabilities for a variety of reasons.

[gliched_robot]

Disagree on Nvidia, most folks fine-tune model. Proof: there are about 20k models in huggingface derived from llama 2, all of them trained on Nvidia GPUs.

[eggdaft]

Fine tuning can take a fraction of the resources required for training, so I think the original point stands.

[nightski]

Maybe in isolation when only considering a single fine tune. But if you look at it in aggregate I am not so sure.

[drcode]

The memory chip companies were done for, once Bill Gates figured out no one would ever need more than 64K of memory

[adventured]

Misattributed to Bill Gates, he never said it.

[phkahler]

Right. We all need 192 or 256GB to locally run these ~70B models, and 1TB to run a 400B.

[Rastonbury]

If anything a capable open source model is good for Nvidia, not commenting on their share price but business of course.

Better open models lower the barrier to build products and drive the price down, more options at cheaper prices which means bigger demand for GPUs and Cloud. More of what the end customers pay for goes to inference and not IP/training of proprietary models

[edward28]

Pretty sure meta still uses NVIDIA for training.

[whywhywhywhy]

>AMD, intel: these companies can focus on Chips for AI Inference

No real evidence either can pull that off in any meaningful timeline, look how badly they neglected this type of computing the past 15 years.

[oelang]

AMD is already competitive on inference

[int_19h]

Their problem is that the ecosystem is still very CUDA-centric as a whole.