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The tiny corp raised $5.1M

geohot.github.io

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Re: The tiny corp raised $5.1M

#121
post #75

> I started tinygrad in Oct 2020. It started as a toy project to teach me about neural networks Shows you what is possible in 2.5 years. Keeps me motivated to learn.

The math isn't super difficult. Some books will try to throw a mess of differential equations at you, but some simple calculus is all you need for backpropagation.

I have been through the math thanks to the youtube videos by A. Karpathy. Deriving some of the differentials, e.g. for batchnorm seems fairly hard (hard as in slogging through something with many steps where you can't make a mistake at any step). But the principles are quite simple - I think by design. If they were hard to compute or reason about then the neural net wouldn't work very well!

Re: The tiny corp raised $5.1M

#122
post #98

He claims there's a $999 AMD card that gives 123 TFLOPS, and his tinybox will cost $15k for 738 TFLOPS. In other words, the tinybox will have 6 of these GPUs, eg $6000 cost price. It seems a steep markup from 6k to 15k, and if the software is open-source, i'm not sure why you wouldn't build your own? Or is it worth 9k for a custom motherboard that can fit so many GPUs. Or can you buy 6-GPU motherboards off the shelf?…

As someone who has built their own deep learning rigs in the $10k BoM range, frankly it's a pain in the ass and I would gladly pay that in the future. I probably will pay lambdalabs a much larger markup.

As someone who bought a deep learning rig for about $12k from lambdalabs years ago, I can't recommend them strongly enough. The support (and not having to deal with building it out myself) was well worth the markup. They're also just really great to deal with.

Re: The tiny corp raised $5.1M

#123
I have some experience in this area, having both worked on machine learning frameworks, trained large models on datacenters, and have my own personal machine for tinkering around with.

This makes very little sense. Even if he was able to achieve his goals, consumer GPU hardware is bounded by network and memory, so it's a bad target to optimize. Fast device-to-device communication is only available on datacenter GPUs, and is essential for models training like LLaMA, Stable Diffusion, etc. Amdahl's law strikes again.

Re: The tiny corp raised $5.1M

#124
post #70

I don't want to cast any judgement, I just want to ask what the initial product is. The claim is they sell computers, and there's a link to the tinybox. There's a $100 preorder, for a 15k computer (I guess I'd have to pay 14.9k eventually?). And then we get a computer that... how do I interact with it? Will it have its own OS? Some flavor of linux? Is the intent to work on it directly, or use it as an inference serve…

I think the tinybox is meant to be a training/inference server meant for tinygrad and filled with those AMD cards. Very likely it will run Linux.

Re: The tiny corp raised $5.1M

#125

> I think the only way to start an AI chip company is to start with the software. The computing in ML is not general purpose computing. 95% of models in use today (including LLMs and image generation) have all their compute and memory accesses statically computable. Agree with this insight. One thing Nvidia got right was a focus on software. They introduced CUDA [1] back in 2007 when the full set of use cases for it…

I know the founders of an AI chip company that taped out and got working chips on their first go. They got their chip done, it’s pretty solid. Chip has great perf and is super power efficient, a solid delivery. I knew they'd nail it and they did.

The SW story is a train wreck, though. The problem basically was that they couldn’t hire any good SW people. As I said I know the founders. They are both genuinely decent guys, they put their own money in so they have some (well, minimal) skin in the game, and they know a ton of expert-level embedded and systems coders with between 20 and 40 years of hard core experience. As far as I can tell, they weren't really able to get anyone that we know in common to join. I certainly did not, and no one I know did either. Last I heard they'd had to hire third choice guys in Europe to do the work and it wasn't going well.

There's a pretty good reason for it, and it comes down to a sociological problem. HW people don’t value SW people. It's just basically true and has been true everywhere I've looked. Maybe if you're doing a system (like a router or maybe a drone) then the HW people will begrudgingly admit that the SW is a major part of the delivery, but that isn't true for chip companies (including chips-on-reference-boards).

You can rest assured that at a chip company, all of the high comp people in the company are going to be on the ASIC team and the SW team will never be on the same tier. The argument is always the same, no matter how many times it bites the companies on the ass and sends them careening into the dumpster: “yes, but the chip without SW is the chip! we can buy SW, if we have to. SW without the chip has zero value.”

Almost every chip company ends up like that, and the kind of low level, experienced SW people that work in the space know to avoid them and work at systems companies instead.

As far as I've been able to determine, with _maybe_ the exception of Cerebras - maybe - this is the situation that has played out at all of the 201x AI chip companies. They get founded by ASIC guys, most of whom have more than a small chip on their shoulder about the relative value of ASICs-vs-SW. These guys are all ex-SGI, ex-Sun, ex-Google, ex-Nvidia, ex-Intel HW guys who saw SW people making a lot more, not just in broader industry terms over the last few years, but at hardware-focused companies. In general, ASIC guys make less than SW guys unless they are the very narrow set of top level architects. IMHO from a value creation standpoint, that is _super unfair_ and I am not here to justify it, but it is how it is. The result poisons ASIC companies. SW people who know what needs to be don't won't go to them most of the time, for good reason, and so they fail.

So I will say, given that, starting with SW first is brilliant.

Re: The tiny corp raised $5.1M

#126
post #53

Earlier quoted context omitted.

I think AMD is going down a different path, ie. ROCm then partnering with ML frameworks further up the stack for first class support. https://pytorch.org/blog/pytorch-for-amd-rocm-platform-now-a...

He mentioned ROCm, and apparently had lack luster experience with it. >The software is called ROCm, it’s open source, and supposedly it works with PyTorch. Though I’ve tried 3 times in the last couple years to build it, and every time it didn’t build out of the box, I struggled to fix it, got it built, and it either segfaulted or returned the wrong answer. In comparison, I have probably built CUDA PyTorch 10 times an…

It's because ROCm is not developed for RDNA (consumer) cards, but CDNA (datacenter) cards. No surprise that he's having trouble with it.

Re: The tiny corp raised $5.1M

#127

Earlier quoted context omitted.

Comma didn't fizzle out. https://twitter.com/comma_ai/status/1578517666632900608

They're slowing burning through their VC money trying to make a business out of the hobbyist market while Cruise and Waymo have fully autonomous cars deployed in SF and are scaling up.

As a very happy user of Comma, I think it is reasonable to say the company is going to fail, but that ignores that the product they created is still awesome. Comma is light years better than any built-in driving assist in any non-Tesla car. And it's comparable to Tesla for far less money.

The reason the company might fail is because their main thesis, being that car manufacturers would just license the self driving tech to somebody else (like Comma), never came about. Car manufacturers are just too conservative. It was a perfectly reasonable bet to make though. Unfortunately they ended up in the business of selling hardware and giving away software for free when they wanted to be in the business of selling software.

Re: The tiny corp raised $5.1M

#128
post #123

I have some experience in this area, having both worked on machine learning frameworks, trained large models on datacenters, and have my own personal machine for tinkering around with. This makes very little sense. Even if he was able to achieve his goals, consumer GPU hardware is bounded by network and memory, so it's a bad target to optimize. Fast device-to-device communication is only available on datacenter GPUs,…

Eh... no? Stable Diffusion works fine on single device. Ditto for smaller LLaMA.

Re: The tiny corp raised $5.1M

#129

For background, "geohot", is George Hotz, who's a known hacker / tech personality[0] This project fits the pattern of his previous projects: he gets excited about the currently hot thing in tech, makes his own knockoff version, generates a ton of buzz in the tech press for it, and then it fizzles out because he doesn't have the resources or attention span to actually make something at that scale. In 2016, Tesla and s…

But....Comma still exists, and they launched the 3rd version of their hardware.

Re: The tiny corp raised $5.1M

#130
post #123

I have some experience in this area, having both worked on machine learning frameworks, trained large models on datacenters, and have my own personal machine for tinkering around with. This makes very little sense. Even if he was able to achieve his goals, consumer GPU hardware is bounded by network and memory, so it's a bad target to optimize. Fast device-to-device communication is only available on datacenter GPUs,…

Eh... no? Stable Diffusion works fine on single device. Ditto for smaller LLaMA.

That's for inference, I'm referring to training.
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