AI Inference: Why Speed Matters More Than You Think (with SambaNova's Kwasi Ankomah)
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All right. Hello, and welcome to the Neuron podcast. Today, we're talking to Kwasi Onkoma. Kwasi is the lead AI architect at Samba Nova Systems, where he specializes in agentic AI and solving the critical challenge of making AI models run fast enough for real-world production applications using Samba Nova's revolutionary RDU chip architecture. So we thought he would be the perfect guest for the Neuron podcast. Just a quick FYI about Samba Nova Systems. Samba Nova builds custom chips, systems, and platforms that let organizations train and run large AI models more efficiently than with standard hardware.
Hi, Kwasi. Welcome to the show. How's it going? Hi, folks. How are you doing? Hi, Grant. How are you? I'm doing really well and super excited to talk to you folks about AI inference and agents. So, yeah, super excited. Awesome.
We're excited to have you here. It's an interesting time and sounds like you guys are doing some neat work.
Yeah, definitely. We've been kind of seeing... a lot of shift in the market. You know, we had this kind of huge focus on training. I think everyone did about how to train these large language models. And now we've kind of seen that around, you know, the biggest bottleneck that we've got now is inference, right? So how do we make things, how do we make inference fast? How do we make it scalable? So we've been really focusing on our architecture in speeding that up and making it more efficient and delivering these solutions to our customers. And my team really focuses on the agentic side of things, which is what I'm super excited to get into, because that is showing why inference matters and all of these calls and the number of tokens is going up.
And that's a really interesting area as well. So, yeah, that's where we're trying to focus on at the moment. Yeah.
Well, I got to ask, okay, so let's just clarify. So very simple, before we get to agents, for our readers and listeners who use ChatGPT daily, maybe don't think about what's happening under the hood. So when you type a prompt into ChatGPT or any other AI and hit enter, what actually happens? Like what is inference in plain English? Yeah.
Yeah, so inference is coming from the word to infer. So it's the model going along and then making a prediction of some sort. So it's taking your input and then it's basically doing the thing that large language models do, which is the next token. And that is the actual process of inference. It goes in, it runs through the model and we get an output and that output keeps going and essentially all you're all that you're doing is that we have a model that has already been kind of put on some sort of architecture and then we are basically giving you the answer or the next token and then of course as you see that stream the next token then goes back in and we get the prediction based on the next token as well so that in a nutshell it's essentially giving you we have a model that's already been trained and we are just giving you the output of that model yeah
Well, you know, so, you know, we always think about the idea that training is the hard part for these massive models. But you and the Samba Nova team are often talking about how inference and actually running them is the real challenge. Can you kind of explain why that is?
Yeah, so I think inference speeds kind of... kind of directly dictates how the user interacts with the application, right? So we've all been there on, you know, your favorite chat application, be that what it may. And when you kind of press that button to inference, right? So when I've talked about inference, you know, you're making a pass through the model and you're getting output at the end. Now that pass can, take a long time depending on the size of the model, the amount of parameters and the hardware that it's running on. Now, if there is a big latency with a real-time application, that does become an issue. And we've seen that for, you know, if you try to run certain models on certain architecture, you can have, you know, a time to first token.
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