Alessio Fanelli: prediction

Transcript context

…iencies that have been gained over the past couple of years, and this is running on a V 100, which is not optimized, you can check the numbers from last year from ML PERF and see that now it's 88,000. Images or samples per second. They use samples. And obviously this is not necessarily apples to apples comparison because you need to check at the fine print as to how they are running this. They are not optimizing for latency. Um, so they are optimizing for 2. 0 first, but even then, like that number is like, It's striking, right? And there are other things that I learned through my time at Nvidia. So, and I can dive more into that, but if you have anything to add there. Yeah, no, that's great. And I think especially the hardware piece is really important. Like, uh, back when you were at eBay, you mentioned the V100 was kind of state of the art. The v100 is about 130 teraflops of kind of like compute the gb200 at fp4 is like 20, 000 teraflops so the hardware alone today got much more powerful and I would love to maybe hear from you how at the time you were thinking about optimizing for the hardware today versus how much of an insight you had into the hardware that was coming especially working at NVIDIA and maybe people have the same discussion today it's like you know Should we optimize for the hardware of today or like for the hardware of tomorrow, because we need the results today, you know, as a business, but sometimes maybe we waste some time. So curious to hear your thoughts. It's interesting to see these two worlds colliding, because when I joined eBay, it was the hardware team where I was in, and then there was the platform team, and then there was the AI research team. And this world decided the whole hardware for the company, and this world lived on this. And this was a small team that was deciding what hardware to use. So it was interesting to see the learning gap between the two worlds. And live through it. And so how do you decide what hardware to use? Where to do your optimizations? I building for the hardware of tomorrow. That is an interesting question. So as you can see, when I was running this in 2018, I was using a V100 for ResNet 50, which is Feels like such an overkill, like you would never today run a ResNet 50, or maybe you would if it's a giant batch workload, but like you wouldn't run this in a GB100 or 200, you would run this on a Jetson device, which is like a hundred dollar device that you can buy. Off the shelf, right? So there clearly were changes to the hardware. It was just more depending on the use case and where you were heading over time. So I am a firm believer that you can't really forecast very well, anything beyond two years, statistically speaking. So in that meantime, it's like, okay, the chips are coming in three years. How does the world look like in three years? I'm not that certain. Going back to the point of that optimization layer. One interesting thing that you can see if you see the slides of NVIDIA is that they compare the same chip over the years. With itself. And they show that the performance optimization improves every year within the same chip. Why is that? And let's speak particularly about computer vision, but the things that made it so that it improved so much over time were obvious things like, for example, I increased the batch size to four, eBay. Because it is still met the latency constraint, right? But just increasing the batch side, there was dynamic batching, which for LLM is analogous to like continuous batching or in flight batching. And then we had obviously quantization and quantization improve over the years, right? Like when in 2018, I was using. Fp16, and Int8 was new. There were talks about different types of quantization, but it took time to develop. And for example, when I was at NVIDIA, we were working on edge devices and we were doing the frameworks for edge devices in particular. And there we, not only did we do Int8, But we did quantization aware training, right? Which basically made it so that the model would perform under those quantization constraints, which we're also seeing here, like where we we've seen in for training and things like that, better convergence with LLMs. But we, we saw that with computer vision.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.