Running a 180B parameter LLM on a single Apple M2 Ultra

> Given how useful GPT-4 is already. Maybe one more iteration would unlock the vast majority of practical use cases.

Unless I'm misunderstanding, doesn't OpenAI have a very vested interest to keep making their products so good/so complex/so large that consumer hobbyists can't just `git clone` an alternative that's 95% as good running locally?

They have a huge cost incentive to optimize it for runtime.

The magic of openai is their training data and architecture.

There is a real risk that a model gets leaked.

What is OpenAI's moat? Loads of people outside the company are working on alternative models. They may have a lead right now but will it last a few years? Will it even last 6 months?

> What is OpenAI's moat?

There’s none. Which is why Sam Altman has been crying wolf, in hope of regulatory barriers which can provide it the moat.

> What is OpenAI's moat?

From what I understand, if you take the absolute best cutting edge LLM with the most parameters and the most up to date model from GitHub/HuggingFace/whatever, it's very far off from the output you get from GPT-3.5 / GPT-4

aka full of hallucinations, not very useful

I don't know if this is the right way to look at it but if what George Hotz said about GPT-4 simply being "8 220B parameter models glued together by something called a mixture-of-experts", from what I understand, OpenAI's moat is:

their access/subsidiized cost to GPUs/infrastructure with Microsoft

the 8 220B models they have are really good/I don't think anything open source matches them/nobody can download "all of Reddit/Twitter/Wikipedia/StackOverflow/whatever else they trained on" anymore like they could given how everybody wants to protect/monetize their content now

and then the "router" / "MoE" piece seems to be something missing from open source offerings as well

OpenAI's "moat" is basically the same as Adobe's or Microsoft's, give or take a metaphor, for Photoshop or Office.

Although see last week for previous responses: https://news.ycombinator.com/item?id=37333747

Indeed they do, however companies like Meta (altruistically or not) are preventing OpenAI from building 'moats' by releasing models and architecture details in a very public way.

Depending on the task, the best open models will outperform GPT-3.5, but would be more expensive to run at comparable speed. GPT-4 is in a league of its own.

it is not really a moat if one engineer can leave openai with all the secret sauce in his head and replicate it elsewhere (anthropic?)

I think it's a safe bet to say it's not altruistic. And, if Meta were to wrestle away OpenAI's moat, they'd eagerly create their own, given the opportunity.

Commoditize your complement strategies can just as likely put a market into a zombie state in the long run.

Adoption and a mass of human feedback collected which is not available in the gleaned data sets.

Here’s another way to think about it. Why does ISA matter in CPUs? There are minor issues around efficiencies of various kinds, but the real advantage of any mainstream ISA is, in part, the availability of tooling (hence this was a correct and heavy early focus for the RISCV effort) but also a lot of ecosystem things you don’t see: for example, Intel and Arm have truly mammoth test and verification suites that represent thousands++ of man years of investment.

OpenAI almost certainly has a massive invisible accumulated value at this point.

The actual models themselves are the output in the same way that a packaged CPU is the output. How you got there matters almost as much or more.

Name one software-based tech company where this isn’t true.

Meta doesn't interact with its users in very obvious ways which MS, Google do. All its models magic happen behind the scenes. Meta can continue to release 2nd best models to undercut others and them going far too ahead. And Open Source community will take it from there. Dall-E is dead.

What if specialized smaller models is the best way ahead for community. I don't care if I am interacting with one big model which can do everything or I have to go to different websites to access specific models. All model sizes will be useful. Smaller models will be frequently used. Bigger less so.

microsoft? gogel? FB?

And if all open source extends their models, they can accrue those benefits back to themselves. This is already how they’ve become such a huge player in machine learning (open sourcing amazing stuff).

I only meant the trained model.

You would need to steal it all over again as soon as the next model is trained.

> And, if Meta were to wrestle away OpenAI's moat, they'd eagerly create their own

Meta is already capable of monetizing content generated by the models: these models complement their business and they could not care less which model you're using to earn them advertising dollars, as long as you keep the (preferably high quality) content coming.

no need to steal the model if training process can be reliably replicated/adopted in clean room implementation with additional optimisations.

startup as legal entity has close to 0 value, most value is in intellectual property which is stored and transmitted by meatbags.

> And, if Meta were to wrestle away OpenAI's moat, they'd eagerly create their own, given the opportunity.

At which point the new underdogs would have an interest in doing to them what they're doing to OpenAI.

Assuming progress for LLMs continues at a rapid pace for an extended period of time. It's not implausible that they'll get to a certain level past which non-trivial progress is hard, and if there is an open source model at that level there isn't going to be a moat.

> Here’s another way to think about it. Why does ISA matter in CPUs?

Honestly the answer is that it mostly doesn't.

An ISA isn't viable without tooling, but that's why it's the first thing they all get. The only ISA with any significant moat is x86, and that's because there is so much legacy closed source software for it that people still need but would have to be emulated on any other architecture. And even that only works as long as x86 processors are competitive; if they fell behind then customers would just eat the emulation overhead on something else.

Other ISAs don't even have that. Would anybody actually be surprised if RISC-V took a huge chunk of out ARM's market share in the not too distant future?

This isn’t really true (or at least, doesn’t apply across the board). Qwen (Alibaba’s open source model) outperforms GPT4 on Chinese language tasks, and I can further finetune it for my own tasks (which I’ve done, and I confirm it’s produces more natural output than GPT4).

Other benchmarks/anecdotes suggest fine-tuned code models are outperforming GPT4 too. The trend seems to be that smaller, fine-tuned task specific models outperform larger generalised models. It requires a lot of resources to pretrain the base model, but as we’ve seen, there’s no shortage of companies who are willing and able to do that.

Not to mention, all those other companies are already profitable, whereas OpenAI is already burning investor cash.

I think Dall-E isn't actually dead, but was merely renamed Bing Image Creator.

That's literally my point. The problem is that there's a massive amount of hidden infrastructure behind those that you don't see and that "oh look everyone has a big model" isn't as impressive as it sounds.

But the open source infrastructure is getting built too. And the infrastructure is mostly independent of the model. This is Falcon 180B running using the code from llama.cpp.