Chapter 1: What is the Maya 200 AI inference chip and why is it significant?
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Chapter 2: How does the Maya 200 compare to the previous Maya 100 chip?
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Chapter 3: What are the performance capabilities of the Maya 200 chip?
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Chapter 4: How does inference differ from training in AI models?
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Welcome to the podcast. I'm your host, Jaden Schaefer. Today on the podcast, Microsoft has made a huge announcement when it comes to AI chips.
Chapter 5: What impact does the Maya 200 have on cost efficiency for AI companies?
They've announced a really powerful new chip for AI inference. So today on the show, I want to break down, it's called Maya 200. what it does, why it's a big deal for what we're going to be seeing with AI in the future.
Before we get into the podcast, I wanted to mention if you want to build AI tools without knowing how to code, without being a developer like myself, I would love for you to try out my platform, AIbox.ai. We have a vibe tool builder where you can describe a tool that you'd want to create, whether that is I just created one that creates profile pictures for people or
Chapter 6: How is Microsoft positioning itself in the AI hardware market?
you upload an image of yourself and it has this right kind of lighting and it kind of creates all of these different things that you're looking for. And, you know, these are great for business portraits or all sorts of other headshots for LinkedIn or other platforms as well. But I just created this tool without being a developer.
I put in a prompt and it linked together a whole bunch of different AI models to create this perfect tool for me. So it's
Chapter 7: What role does power efficiency play in AI workloads?
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Now let's get into the episode.
Chapter 8: How will the Maya 200 influence future AI model deployments?
Like I was saying, Microsoft, they just launched their newest custom AI accelerator. It's called the Maya 200. So This is their very purpose built. It's a silicon platform, and they are aimed at one of the most expensive and also it's one of the most complex parts of modern AI systems. If you're looking at this from kind of like an operational perspective, and that is large scale inference.
The Maya 200 is the successor of the Maya 100, which Microsoft, they actually launched that one back in 2023 as it was kind of like their first serious in-house AI chip that they were creating. This new generation, now that they've made the 200, is a really big step forward. So there's a couple of things that it does. Number one is just raw performance.
And then also how tightly the chip is integrated into Microsoft's kind of broader cloud and also AI stack. So according to them, the Maya 200 has more than 100 billion transistors, and it's capable of delivering up to 10 petaflops of performance in a four bit in four bit precision, and roughly five petaflops in eight bit, which is a massive increase over the last generation.
And I think it's really trying to optimize for just running larger language models efficiently and doing this in production. It's interesting to me seeing Microsoft get into the chips game. There's a lot of competitors in this space, but not a lot of competitors that could really compete at this level.
And Microsoft, I think, sees just how much money they'll have to spend, let alone, you know, not to mention just how they're not able to customize everything the way they like if they're if they're using outside suppliers for this. So it's interesting for me seeing them get into this.
And for those that are curious, right, inference is essentially just the process of executing a training AI model to generate outputs, as opposed to training, which involves teaching the model in the first place, right? So we have inference, which is getting it to generate for you.
And what's interesting is, I think we talk a lot about the GPUs involved from NVIDIA, if you want to train an AI model, and just you know, how intense that can be. And yes, it does cost a lot of money, it is very intense. But I think it's also important to remember, there are millions of people around the world using these AI models.
And we also need to optimize the tech stack for people that are generating stuff. So I think, well, training oftentimes gets a lot of kind of like the headlines and people talk about it a lot because it's basically this kind of massive upfront compute demand, right? Like in order to train one of these models, you're spending millions and millions of dollars.
I think inference is quietly becoming a really dominant cost center for a lot of these AI companies because their models are, you know, getting deployed to millions of users. That's chatbots. And then if you look at Google, that's like all of the search tools. You have copilots for Microsoft and a bunch of others and a lot of the enterprise software. So
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