Introducing Z1T: Our first family of transformer-like models made for sparse probabilistic hardware like Z1 Achieving up to 140x energy efficiency gains over GPUs and revealing a new scaling law for sparse transformers Read the blog: extropic.ai/writing/z1t

Extropic founder @beffjezos explains how Z1 chips sitting alongside GPUs could make AI data centers 100X more energy efficient: "The reason this hasn't been really explored is that there's not a huge upside for sparsity on traditional accelerators. Because GPUs aren't really good if you try to do matmuls that are sparse." "It's like shipping a big freight ship across the ocean with a bunch of empty containers. You're wasting a lot of gas. Whereas for us it's compute and memory, it's naturally sparse. We get a huge energy efficiency gain there" "Nowadays everybody's obsessed with tokens per watt at a data center level. If you incorporate TSUs like Z1 into your data center and park them next to your GPUs, you can get 100X energy efficiency gain at a data center level." "People are struggling to power these data centers. It's become the bottleneck. There's a bunch of GPUs sitting on the sidelines right now that can't be powered on, and people are thinking of creating a Dyson swarm to get more power. Why don't we just compute more efficiently first?" @extropic

It's about to go down. 🦾🥊👀

the first demo of thermo robot brains ever?

Extropic founder @beffjezos explains why his new Z1 chip uses 10,000x fewer transistors than a GPU to run the same AI algorithms: "pbits are not traditional bits that are definitely zero, definitely one. They're oscillating between the two. It's like a light switch dimmer. You ever have it flicker when you're at the edge? It's kind of like that." "It's a fractional bit. I could be like, hey, 20% of the time I'm zero, 80% of the time I'm one. That would be a fractional flip." "The most interesting thing is that you can do all sorts of AI algorithms with about 10,000 times less transistors than if you were to do them digitally." "If you go back to the ancestry of neural networks, they came from something called energy-based models from Hinton." "We can do that specifically with Z1. We have literal Boltzmann machines in the hardware. They're very dense, and they use very few transistors, because each transistor is operating in the regime that would be usually considered janky. It's so low power, it's flipping all the time." @extropic

Scaling Thermodynamic Computing: From One to One Billion Announcing: - Our newest chip: Z1 + hardware systems - Torx: our software framework for stochastic programming - Thermalizers: our thermodynamic compiler - Our new live simulator API Read more : extropic.ai/writing/from-one…

Update from CEO @GillVerd on @tbpn today

📡

Our paper on Denoising Thermodynamic Models is now published in Nature's npj Unconventional Computing. We demonstrate thermodynamic hardware built on the primitives validated in X0 could unlock generative modelling at 10,000x lower energy than GPUs. nature.com/articles/s44335-0…

Unboxing the new XTR-0 from @extropic for my strangers @Strangeworks in honor of @GillVerd/@beffjezos big day! I can't say how awesome it is, but I will tell you it's all of the awesome! Looking forward to sharing more in the coming months.

Einstein revolutionized physics as a professor at ETH Zurich. Now, on June 10th, the same halls will host the Probabilistic and Thermodynamic Computing Hackathon. Hands on with Energy Based Models and the tools and frameworks from the @extropic team led by CEO @GillVerd. They are building physics first AI chips for the post GPU era. Sponsored by ETH Future Computing Laboratory (EFCL), ExtropicAI & myself (Uncertain Systems) $3000 prizes. Under 80 seats Sign-up now: pc-hackathon.openlesson.acad…

Great video by @AnastasiInTech on the physics behind our thermodynamic computing devices @extropic