With the world constantly generating more data, unlocking the full potential of AI means a constant need for faster and more resilient hardware.

In this episode – the second in our three-part series – we explore the challenges for founders trying to build AI companies. We dive into the delta between supply and demand, whether to own or rent, where moats can be found, and even where open source comes into play.

Look out for the rest of our series, where we dive into terminology and technology that is the backbone of the AI, how much the cost of compute truly costs!

We currently don't have as many AI chips or servers as we'd like to have.

How do I get access to the compute that I need?

You're looking at some very large investment projects that take some time to adjust.

You can look at AI just as a new application, but honestly, I think it's probably a better way to look at it as a different type of compute.

With software becoming more important than ever, hardware is following suit.

And with the world constantly generating more data, unlocking the full potential of AI means a constant need for faster and more resilient hardware.

That is exactly why we've created this mini series on AI hardware.

In part 1, we took you through the emerging architecture powering LLMs from GPU to TPU, including how they work, who's creating them, and also whether we can expect Moore's law to continue.

But part 2 is for the founders trying to build AI companies.

And here we dive into the delta between supply and demand, why we can't just print our way out of a shortage, how founders can get access to inventory, whether they should think about renting or owning, where moats can be found, and even where open source comes into play.

You should also look out for part 3 coming very soon, where we break down exactly how much all of this costs, from training to inference.

And today we're joined again by A16z special advisor Guido Appenzeller, someone who is truly uniquely suited for this deep dive as a storied infrastructure expert with experience like.

CTO for Intel's data center group dealing a lot with hardware and the low level components.

So it's given me sort of, I think a good insight how large data centers work, what the basic components are that make all of this AI boom possible today.

Despite working with infrastructure for quite some time, here's Guido commenting on how the momentum of the recent AI wave is shifting supply and demand dynamics.

The biggest thing that is triggering that is just the crazy exponential growth of AI at the moment.

I think nobody expected how quickly it would move.

And that has just created a demand which at the moment the market can't fulfill.

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recent article, Guido even stated that some reputable sources indicate that demand for AI hardware outstrips supply by a factor of 10.

Here's him commenting on how that dynamic is impacting competition.

So for some of our portfolio companies, finding the compute capacity that they need to run their applications is actually a real challenge.

We have some bottlenecks on the chip manufacturing side.

We have some bottlenecks on building the actual cards.

But probably the biggest thing that is striking that is just the crazy exponential growth of AI at the moment.

Maybe this is a silly question, but what really is stopping companies like Intel, like Nvidia, from going and 10xing their production?

Like, is that on the roadmap where we're just going to see a lot more chips and we won't see this discrepancy between supply and demand?

Or is there something more complex at play?

It's a bit more complex because if you want to make a chip, right, the way you do it is you make it in a foundry, right, which are extremely large, extremely complex.

Intel makes chips on their own foundries, but most companies manufacture with Taiwan Semiconductor TSMC, right?

And they are capacity constrained, right?

You often have to reserve capacity long in advance.

So, you know, it might be for a certain process, which you don't want to use this capacity, but for another 1 that you do want to use, they don't have the capacity.

And you could just say like, well, in that case, let's just build more fabs.

But Building a fab takes you a couple of years and probably a couple of billion or 10 billion of investment.

So you're looking at some very large investment projects that take some time to adjust.

And that's sort of what prevents us from reacting more quickly at the moment.

While some countries are making major multibillion dollar investments in new semiconductor production plants, AKA fabs, these will take time to scale.

And there are also no promises, given that expertise is concentrated in a few companies.

So with demand not subsiding, what does this mean for who gets access to the supply available?

It doesn't sound like the demand is going to subside, especially because we see this really, what seems like intrinsic relationship between the power of these models and then the compute that's thrown at them.

And so if we do expect demand to continue, I guess the question that arises is, how is this demand allocated?

So how does a company, let's say if I'm a founder today, how do I get access to the compute that I need?

Is it just who's willing to pay the most?

At the moment, capacity is expensive wherever you go.

You know, I tried just to run some personal experiments, tried to reserve an instance of the cloud service providers a few days ago and they just didn't have any.

And what we're seeing is that often in order to get access to the newer cards and newer chips, right?

If you want it at scale, you have to pre-reserve capacity.

So Often these are negotiations between a company and a large cloud where you say, okay, I need this many chips for this amount of time.

What they'll often ask for is they ask for a certain time commitment.

So it'd be like, okay, we can give you this many chips, but we want you to sign basically that you get them exclusively for 2 years and you pay for that amount.

I think OpenAI was in the news for that, right?

Where you have investment deals, where for example, a cloud provider comes in and invests in a company, and as a result, the company gets capacity.

So we're seeing all kinds of deals being struck.

It's not just a matter of getting access to compute.

It's about ensuring you get access to the kind of compute tailored to your needs.

What would you say in terms of the considerations that they should be keeping in mind.

Really how much should founders know about hardware?

And again, selecting which hardware to use.

I think the first question, honestly, I would ask is, do you really need to consume the hardware directly?

Or do you really just want to consume something that runs on top of the hardware, right?

If I want to generate images with stable diffusion, for example, for my mobile phone app or something like that, it might be easier to go to a SaaS company like Replicate, for example, that essentially will host the model for you, where you just pay for access to the model, and they send you back the generated images, and they will manage all the provision of computer infrastructure and will find the GPUs for you.

If you do want to run your own model, I think my number 1 advice would be to shop around, right?

The large clouds, in my experience, are not always the best option, right?

If you price it out, we've seen that the startups typically are more likely to go with specialized clouds like CoreWeave or Lambda, right?

They're basically specialized in providing AI infrastructure to startups.

Shop around, look at the different offers, compare prices.

And when you're shopping around, in addition to price, which I feel like is a major motivating factor, what other factors are there in terms of these other companies who maybe aren't the big clouds?

How are they differentiating relative to 1 other?

How are they standing out in that market?

There's a whole sort of decision tree there.

But the first thing is, 1 thing that often drives the decision is how much memory do I need in my cards, right?

If I have a small image model, right, I might be able to work with a more consumer grade card, which is much cheaper per hour if I reserve it in a cloud, versus if I, for example, train a large language model, I not only need a card with the most memory I can find, but I probably want to have as many cards as possible in 1 server because communication between them matters.

And I may even care about the networking fabric behind it.

Some of the very large models, you actually network constraints in terms of how quickly you can train them.

So it really becomes a question of what's your objective, is inference, is it training?

And based on that, you figure out what the card is, what kind of server you need, what kind of fabric you need between those servers, and then you sort of can decide what the right fit is for your application.

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Even prior to this AI wave, compute was a major line item for many software companies.

And the calculus of leaning on the easily accessible cloud versus bringing infrastructure in-house was becoming an increasingly important consideration.

Here is Guido touching further on that very calculus in today's era and where scale comes into play.

It's a major line item for many companies.

And this is even before the AI revolution, you could say.

So how do you think about, again, like how that impacts different companies' bottom lines and whether they really factor that in to having their own allocated GPUs versus using something more like Replicate.

You really have to figure out what is the right fit for you.

And it probably depends a lot on the scale at which you need them, right?

If you need a lot, you're frankly, you have to pre-reserve them, right?

You have to have your own, there's just no way around that.

If you need a smaller quantity, you may be able to reserve them on a more short-term basis, or you have various models where you can consume only while your application runs, but at a higher price.

And so this really comes down to what kind of load do you have?

We're typically seeing if somebody is training, they're more likely to do a long-term reservation for a GPU because you want to make sure you have access to it.

If somebody has more continuous workloads where availability is important, like if I just do inference, but I want to make 100% sure that if a request comes in, I can service it, I can never be down, right?

I probably need to reserve capacity as well, right?

other hand, if I have more batch jobs where it's like, ah, this job runs an hour later, that's not the end of the world, then you probably can go with variable capacity and just reserve it ad hoc.

But it's really a conversation of what is the usage pattern, what's your demand pattern?

And from that comes the best pick for the partners that you work with.

We've seen that companies, even prior to AI, have benefited from building their own infrastructure by basically bringing that in-house because before that they were renting and they were paying a lot to rent that compute.

Do you think that will be a differentiator for companies moving forward or how should founders be thinking about that relationship between owning the infrastructure and renting it?

Owning the infrastructure comes with cost as well, right?

Because you need to now hire people that run it, right?

You need to get money for the CapEx and so on.

So my guess is that most early stage founders and probably even most mid-stage and late-stage founders are better off by renting capacity, renting a cloud, or using a consumer SaaS service.

If you have really, really specialized needs, you may just not find anybody who has exactly the kind of hardware that you need.

There might be some cases where you have geopolitical concerns.

And there's probably a certain scale where it makes sense for you to run your own data center.

If you're spending $10 million a year, you're probably still undercritical.

If you're spending $100 million a year on infrastructure, that may be a reason to look into options for your own data center.

But if everyone is competing for the same compute, are there other ways to stand out?

You could say a moat is getting access to different training data, But that actually doesn't necessarily have to do with compute or money being thrown at the problem.

It's getting access to differentiated data.

If you have access to differentiated data, that could be a mode.

I mean, it's a bit more subtle because, look, if you had an area where there's just not much public training data, that's probably right.

There might be areas like in finance or so where that's the case.

But for example, for a large language model, it turns out that just making a larger model and training on more data has more benefits than just absorbing more knowledge.

It also means that it's better in reasoning and understanding abstract context and answering really complex multi-stage questions and so on.

So probably, if I have to guess, I think the future will be that we'll still train on all the data we can find, right?

And then maybe you fine-tune, meaning you do some additional training on a particular problem domain with your private data, if that makes

And so you first go to elementary school to learn reading and writing, and then you go to your vocational training for the specialized job that you have to do in the future.

Another important question worth addressing is who can realistically compete.

If compute is expensive, will all the largest, most heavily capitalized companies win since they can build the largest models with the most data?

As 1 of many emerging examples, Vicuña was created by fine-tuning Meta's Llama1 model for chat.

The cost of fine-tuning added only an additional $300, but the result is competitive with much larger models like chat-gpt or BART.

So what might this example and a growing number of open source projects tell us about the future of open LLMs?

So first of all, in general, larger models, if they're everything else being equal, perform better.

So the really small open source models that we're seeing out there today, they're not yet at a level of a GPT 3.5 or GPT 4.

And there's actually a website that runs sort of regular bake-offs where they basically ask users to prepare answers.

And it seems to be pretty clear that the large ones are still a little bit ahead.

That said, we're making big advances there, and we're figuring out a couple of things.

So 1 thing we've learned is there's something called the Chinchilla scaling laws that basically give us an idea how does data correspond to model size.

And if we over-train, so don't train as efficiently as we could, we can actually get potentially a smaller and better model.

So you can match the performance of a large model with a smaller model if you train it more.

That reduces model sizes and the trend at the moment is to make slightly smaller models and train them more to get equal performance.

The second thing is that when we talk about models, there's models for slightly different purposes, right?

You have the base large language models, all that trained and practically speaking as completing text, right?

Literally, how you train them is you give them text and say, guess the next letter.

And then you tell them, nope, that was wrong.

I didn't, and basically back propagate the, how they predict.

And they're really good at that completing text, right?

That's not quite the same that you want from a chatbot model that you can tell to do something.

So there's usually another step afterwards, which is called fine tuning for instruction following or for chat specifically, where basically I tell a model, look, if somebody asks you to come up with a list of to do item, like a list of steps, how to make pizza,

This is roughly what I expect you to answer, right?

These models are very good in learning these things.

So basically you first train them just complete text, and then you train them how to react to human requests and instructions.

So that's called instruction fine tuning.

Lama, for example, that was a Facebook model where they published the weights for researchers.

And then some people took that and they fine-tuned it, meaning they took a bunch of instruction for things to turn it into alpaca or vicuña, which is a much, much nicer model in terms of interacting with it, right?

And so The biggest challenge at the moment we have in the open source side is that there's currently no large open source LLM out there.

There's currently nothing in that weight class that's open source and that people could use to fine tune or to play with

It is worth noting that since this recording, several more open models have been released, including Llama 2 with 70 billion parameters and an open license, unlike its predecessor Llama 1.

Another 40 billion parameter open source model, Falcon, was released as well.

Both of these are still dwarfed in parameters compared to closed models like OpenAI's GPT-3 at 175 billion parameters, or GPT-4 at an estimated

trillion parameters, although the latter is speculated to be a collection of multiple smaller models.

However, parameter count is not the only driver of performance.

For example, while Llama2 has fewer models than GPT-3, its performance is actually much better due to being trained on more data.

In fact, Lama2 is currently comparable to GPT-3's successor, GPT 3.5, the current default of chat-gpt.

And as many of these models continue to get larger, we may see some models compress, becoming more efficient and enabling inference on your device.

You already mentioned stable diffusion can run on your computer's GPU.

Because right now they are all hosted by these companies, right?

They're trained by these companies on their dedicated servers.

And then even if you interface with chat GPT, it's running that inference for you.

expect to see that change at all as compute becomes cheaper, maybe more decentralized, or how would you think about that?

And we're speculating a little bit here, but my guess is we will, right?

And we're seeing some of these smaller models getting pretty good.

They run on your laptop or even your phone.

We're starting to see stable diffusion implementations that run well on phones, right?

I mean, they take a couple of 10 seconds to create an image, which is comparatively slow, but there's certain applications where that's acceptable.

So my guess is as both the devices get faster and the models get more optimized, this will be a trend that we see more and more.

And in the future, it might just be part of the operating system to have a basic large language model or basic image generation model.

Maybe I'm off base here, but we've talked about how expensive compute can be and how ultimately that can be a major line item for companies.

And I guess probably the model training will remain with those companies and not necessarily on folks' devices.

But in terms of the inference, I assume that's still a pretty significant cost.

And in a way, if someone is able to run that locally, doesn't that disjoint the company from having to pay for that compute because it's running on, let's say, someone's MacBook GPU?

I mean, look, if I can generate an image of my phone directly, all it takes is some battery power and it gets a little warm, right?

At the same time, there's probably going to be a little bit bifurcation there around quality and parameters, right?

You can run things locally, but you can probably run them a lot better in the cloud, right?

Because you have a much bigger server there.

So it probably depends a little on what you want to do.

If I just want to have a better spell checker that checks my email or maybe just some simple completion, that's perfectly fine.

On the other hand, if I want something that is more write a good speech or summarize a complex text, they might be like, oh, that I'm going to run the cloud because it takes so many more operations.

Hopefully, this is getting your wheels spinning in terms of what can be built.

And here is Guido speaking to how this presents a fundamentally new stack and what that means in terms of opportunity.

It feels like this really is like this massive wave, this renaissance of innovation.

You can look at AI just as a new application, but honestly, I think it's probably a better way to look at it as a different type of compute, right?

We traditionally build software by composing algorithms in a way that we understand well and where the end result was programmed or sort of bottoms-up constructed.

Now we have a second type compute where we just train a large neural network.

And the big advantage is we don't actually need to know how to solve a problem.

As long as the network can figure it out, the neural network can figure it out, we're fine.

And that opens up a bunch of new applications.

But it also means you need a completely different stack in terms of all the different pieces.

You probably want vector DBs to retrieve context.

And You want different types of hosting providers that are good in hosting these models and providing them to you as a service.

It's a whole Cambrian explosion of creativity as a whole, a new ecosystem forming.

And I think there's a ton of opportunities to build great companies.

I think that paints a pretty incredible picture of opportunity across the stack.

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Chasing Silicon: The Race for GPUs