More Than Moore

As I see this is : TT-Metalium [1] = a low level API [1]

"TT-Metalium is a platform for programming heterogeneous collection of CPUs (host processors) and Tenstorrent acceleration devices, composed of many RISC-V processors. Target users of TT-Metal are expert parallel programmers wanting to write parallel and efficient code with full access to the Tensix hardware via low-level kernel APIs."

https://tenstorrent-metal.github.io/tt-metal/latest/tt_metal/programming_model/index.html

[1]

"The software stacks come in two varieties – a high level and a low level. The high-level is called TT-Buda, using higher-level APIs to get things up and running, along with interfaces into modern machine learning frameworks. The lower level is TT-Metalium, which provides fine-grained control over the hardware for custom operators, custom control, and even non-machine learning code. Tenstorrent states that there are no black boxes, no encrypted APIs, and no hidden functions." https://morethanmoore.substack.com/p/unboxing-the-tenstorrent-grayskull

More context: This is related to today's release of the Spring Top 500 list (https://news.ycombinator.com/item?id=40346788). Aurora rated 1,012.00 PetaFLOPS/second Rmax, and is in 2nd place, behind Frontier.

In the November 2023 list, Aurora was also in second place, with an Rmax of 585.34 PetaFLOPS/second.

See https://www.top500.org/system/180183/ for the specs on Aurora, and https://www.top500.org/system/180047/ for the specs on Frontier.

See https://www.top500.org/project/top500_description/ and https://www.top500.org/project/linpack/ for a description of Rmax and the LINPACK benchmark, by which supercomputers are generally ranked. The Top 500 list only includes supercomputers that are able to run the LINPACK benchmark, and where the owner is willing to publish the results.

The jump in Aurora's Rmax scope is explained by Aurora's difficult birth. https://morethanmoore.substack.com/p/5-years-late-only-2 (published when the November 2023 list came out) has a good explanation of what's been going on.

I mean you could paste into ChatGPT:

> Please summarise https://morethanmoore.substack.com/p/the-race-to-2nm-risc-v-chips-in-japan

But this is my _personal_ takeaway: there are companies building (or trying to) Risc-V cores at advanced geometries. That is somewhat unheard of for a rather new and not-yet popular core. This suggests a lot of momentum for it. It's actually a new Japanese company called "Rapidus" that has some major backers and it's working with Tenstorrent (which has had a lot of buzz over the last couple of years). So it's exciting because a new company is entering the ring, from a new country, and they are benefitting Risc-V which is gaining traction.

There is another article on this here: https://www.anandtech.com/show/21281/tenstorrent-licenses-risc-vcpu-ip-to-build-2nm-edge-ai-accelerator

> For whatever reason, Acorn dropped the ball.

Acorn's CPU division is the most successful CPU design house in the world and sells around 10x more than all forms of Intel and Intel-compatible chips put together.

It was named after its first product, the Acorn RISC Machine: ARM. It is still called Arm Ltd. today.

Arm alone is one half of the entire CPU market.

https://morethanmoore.substack.com/p/arm-2025-q4-and-fy-financials

An Acorn-compatible CPU is inside half of the processor-powered devices in the world.

How is that "dropping the ball"? It is the most successful processor design of all time, bar none.

Dev kits:

- Grayskull e75 | drawing 75W | 96 Tensix cores | 1 GHz clock | 96 MB SRAM | 8GB LPDDR4 @ 102.4 GB/s | $599

- Grayskull e150 | drawing 200W | 120 Tensix cores | 1.2 GHz clock | 120 MB SRAM | 8GB LPDDR4 @ 118.4 GB/s | $799

It will be interesting to see their inference performance, compared to graphics cards. Will they be interesting for home labs?

I found one interview with unboxing a preview version (if I understood correctly), with some background info, but no performance numbers: https://morethanmoore.substack.com/p/unboxing-the-tenstorrent-grayskull

cuLitho isn't an AI process. Ian Cutress covered that in his article
https://morethanmoore.substack.com/p/nvidia-enabling-computational-lithography

> Of course when speaking about NV, it’s hard not to mention machine learning. Currently cuLitho is purely a mathematical solution tool, and not a machine learning accelerated tool. The engineering team we spoke to, when asked about adding ML tools to something like this, had a gleam in their eyes and essentially said ‘that would be the dream’. So not yet, but perhaps in the future.