Well, I trust you're pretty good at it because you just raised a lot of money [laughter], and so clearly some folks believe in the way that you talk about it. Tell me, why is d-Matrix's chip better than Nvidia, as I talked about the stats up front? Okay. You talk about faster, more cost-efficient, it's cheaper. How are you doing this?
Yeah. So I think let's peel the onion on that a little bit, right? And let's take a step back. So first of all, d-Matrix is focused on inference, right? We don't focus on training, which is the other piece of AI computing which, you know, Nvidia GPUs are extremely good at, right? When we started the company in 2019, we had a singular mission: focus on the other piece, which is inference — where AI actually gets deployed, where monetization of AI happens, decisions get made, productivity is unleashed.
And, you know, back in 2019, I'll be honest, you know, when I would visit investors and talk to them about inference, it was a word that most people didn't even understand, right? They were like, what is inference? And now, post ChatGPT, post DeepSeek, the whole world is talking about inference. How do we make inference available to all of humanity? Every human on the planet wants to use compute for inference specifically. Very few people want to train models, but everybody wants to use AI and use inference, right? So it's not really a one-size-fits-all problem. It is not like you can build one chip that serves all of humanity's needs for inference, right? So you really need something that is different strokes for different folks.
And when we talk about how we are better than GPUs at certain types of workloads, certain types of applications, you know, the 10x faster, 3x energy efficient, 3x cost efficient — again, the whole premise of the platform that we built at d-Matrix was around optimizing for efficiency. And when we talk about efficiency, it's about doing more with less, right? And what are the three most critical resources that humankind cares about? Well, it's time, it's energy, and it's money, right? So if you find a way to do more compute, more inference compute, with less time, with less dollars, with less energy, that would be a clear win.
Now, we also realized that we really can't do this for every single application out there, right? Because, you know, the GPU today is a more general-purpose acceleration engine. What we were building is a more dedicated accelerator for inference, and we started our journey with a focus on what we call specific applications that run on smaller language models. That's what we focused on today, and...
That's right. That's right. What we're hearing from most customers is like, look, you know, most applications, enterprise applications today, run on these smaller sub-100-billion-parameter models, right? The frontier models that are, you know, call it trillion-parameter models, you know, more suited for AGI, right? More general-purpose AI. So there is going to be a segment of companies that chase AGI, frontier models — that is not what d-Matrix is focused on. We're really focused on the enterprise-class smaller models.
