YouSaid · the spoken record

Raja Koduri

lines on the record: 25
first: 2023-08-02
most recent: 2023-08-02
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Full of opportunities, right? I mean, we're rebuilding a stack. You can look at AI just as a new application, but honestly, I think it's probably a better way to look as a different type of compute. We traditionally build software by composing algorithms in a way that we understand well and where the end result was programmed as a bottom-sub-constructed. Now we have a second type compute where we just trained 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, right? 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, right? You probably want vector DBs to retrieve context. You don't want different types of hosting providers that are good in hosting these models and providing them to as a service. It's a whole Cambrian explosion of creativity as a whole and your ecosystem forming. And I think there's a ton of opportunities to build white companies.”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, totally. I mean, look, if I can generate an image on my phone directly, all it takes is some battery power. It gets a little warm, right? And that's it, right? So that's a huge advantage. 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 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. I can run that on my phone. 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.”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“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. 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?”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“That's a really good question, 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? Which I would have never thought, right? 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. In the future, it might just be part of the operating system to have a basic large language model, a basic image generation model.”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“The current default of ChatGPT. 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. Already mentioned stable diffusion can run on your computer's GPU. Do we expect to see more of that? Because right now they are all posted by these companies, right? They're trained by these companies on their dedicated servers and then even if you interface with ChatGPT, it's running that inference for you. Do we expect to see that change at all as compute becomes cheaper, maybe more decentralized, or how would you think about that?”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“Stuff to do a list of steps how to make pizza, right? This is roughly what I expect you to answer, right? These models are very good in learning these things. So whether you first train them, just complete text, and then you train them how to react to human requests and instructions for instruction fine-tuning. And so 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 vicuna, which is a much, much nicer model in terms of interacting with it. For humans, it's much, much, much more useful. And so the biggest challenge that...”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“And if we overtrain, so don't train as sufficiently as we could, we can actually get potentially a smaller and better model, right? So you can match the performance of a large model with a smaller model if you train it more. So that's interesting. That reduces model sizes. And the trend at the moment is you make slightly smaller models and train them more to get equal performance. The second thing is that when we talk about models, models for slightly different purposes, right? You have the base large language models. All they're trained in practically speaking is 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, or yes, that was right. I didn't investigate, I propagate 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 or from a 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 little bit of”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“So, first of all, in general, larger models, if 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 one 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.”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“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? Or what role does open source play? As one of many emerging examples, Vicunia was created by fine-tuning META's Llama One model for chat. The cost of fine-tuning added only an additional $300, but the result is competitive with much larger models like ChatGPT or BART. So, what might this example and a growing number of open source projects tell us about the future of OpenLLMs”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“That's true 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 multistage 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. And then maybe you fine-tune, meaning you sort of do some additional training on a particular problem domain with your private data, if that makes sense.”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“But if everyone is competing for the same compute, are there other ways to stand out? Where's the moat here? 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.”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“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 service, right? There's a couple of exceptions. If you have really, really specialized needs, right, 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. Your data is just too sensitive that you need to run your own data center. And there's probably a certain scale where it makes sense for you to run your own data center. But it's a pretty large scale, right? If you're spending $10 million a year, you're probably still under critical, right? If you're spending $100 million a year on infrastructure, that may be a reason to look into options for your own data center.”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“We have 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”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“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, frankly, you have to pre-reserve them. 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, right? And so this really comes down to what kind of load do you have, right? What 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. On the other hand, if I have more batch jobs where it's like, well, this job runs an hour later, that's not the end of the world, then you probably can go with variable capacity and just preserve it ad hoc. But it's really a conversation of what is the user?”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“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 one another? How are they standing out in that market?”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“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? Let's take an example. 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 one advice would be to shop around, right? There's a fair number of providers. 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? That are basically specialized in providing AI infrastructure to startups. Shop around, look at the different offers comparable.”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“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. And cost is not the only factor here. 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?”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“There's some of that right at the moment, capacity is expensive wherever you go. You know, I tried just to run some personal experiments, try to reserve an instance bone of the cloud service providers a few days ago, and they just didn't have any. It's like, no, not available. 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'll be like, okay, we can give you this many chips, but we want you to sign basically that you get them exclusively for two years and you paid for that amount. I think OpenAI wasn't in the news with that, right? Where you have investment deals, but for example, cloud provider comes in and invests in a company as a result, the company gets capacity. So we're seeing all kinds of deals being struck as with any scarce resource, right? There's a lot of deal making going on.”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“Don'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? Who decides this? Is it just who's willing to pay the most or how is that supply being distributed?”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“And they are capacity constrained. You often have to reserve capacity long in advance. There's different processes. So, you know, it might be for a certain process which you don't want to use, there's capacity, but for another one 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.”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“A bit more complex because if you want to make a chip, right? The way you do it is you make it on Foundry, right? Which are extremely large, extremely complex. Intel makes chips on their own foundries, but most companies manufacture with Taiwan semiconductor TSMC, right?”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“Currently, don't have as many AI chips or servers as we'd like to have. So for some of our portfolio companies, finding the compute capacity that they need to run their applications is actually a real challenge, right? There's a whole value chain behind that. It's a combination of many things. We have some bottlenecks on the chip manufacturing side. We have some bottlenecks on building the actual cards. These development cycles take some time. So it's a combination of factors. But probably the biggest thing that is triggering there is just the crazy exponential growth of AI at the moment.”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“The biggest thing that is triggered is just the crazy exponential growth of AI at the moment. AI has been booming since mid-last year. 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.”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source
“Looking at some very large investment projects that take some time to adjust. We're rebuilding a stack. You can look at AI just as a new application, but honestly, I think it's probably a better way to look at a different type of compute”
2023-08-02 · a16z Podcast · Chasing Silicon: The Race for GPUs · IDENTIFIED FROM THE TRANSCRIPT · source