Seems to be somehow some kind of offshoot from or connected to Tenstorrent, which is just down the road. Founder looks like he was/is maybe at Tenstorrent and previously associated with Keller?

Always fantasize about applying at Tenstorrent, but wrong side of Toronto. 2 hour commute.

Works well, I remember driving by the ATI building as a kid.

This is a new architecture. It's a non-vonn neumann device.

There are always uses for outdated models.

Claude Code is still using haiku 4.5 from ages ago for explore subagents for instance. Not to mention production uses like customer service that only need to be "good enough"

2 to 3 months optimistically assuming everything goes smoothly and is fully automated.

6 months or even a year if something goes wrong in the fabrication process and you need to update things.

If they do more standard asic design, it could be a lot longer as the design needs to be validated on an FPGA cluster, which would necessarily need to be very big for something like a LLM. Easily up to 2 years.

There's a reason chatjimmy isn't demonstrating newer models and why they only show of an 8B model.

I mean even if it take a few months, it'll still be out of date. But there was a hypothetical when it came up in Feb, would you want Qwen 3.5 at like 10k tokens per second.

At the time people were no doubt saying yes but now 3.8 is out, is that still desirable?

> What does it take to go from here to a model on a pcie card or an m.2 card

It is already that.

> Will "intelligence" become much like a gpu

As an option among the implementations.

> Are we a couple years away

They could mass produce now, but it makes no sense at this rate of improvements in the models.

Imagine a multi-modal model with 1000's of tokens per second. Realtime inference for a host of applications. This is a BIG deal and will change the landscape in unfathomable ways.

The https://chatjimmy.ai demo was impressive.

Once models settle down this makes sense. Imagine a cartridge with a physical model on it. You purchase a cartridge and stick it in your computer/phone/server. Want to upgrade? By a new 'cartridge'.

This should bring inference cost down dramatically, I wonder how OpenAI/Anthropic feel about that.

Wouldn't this mean someone with sufficient hardware could lift the SOTA model weights off the chip? Or are you saying that these chips would only be used internally by these companies and not sold to the public?

Then we can have machine psychologists pull cards when they run amok.

Having a base model ASIC as a physical piece of hardware makes me think of the early days of microcomputer desktop stuff where having a socketed ROM or PROM was a key piece of hardware, and people actually knew/cared what ROM was on their system's motherboard.

Imagine if like instead of having a specific Mac Plus ROM, you had a thing that looks like a fat ASIC that can hold models sitting on a slotted daughtercard directly next to the CPU and RAM.

we have not converged at all, if you look at how different the chinese models in terms of architecture you can guess that the labs are experimenting a lot as well. we are seeing all different types of hybrid architectures, different attention methods and so on. Of course on a high level its still a transformer but if you take a proper look we are seeing more divergence then a convergence.

The technical aspects of SOTA models are not publicly documented. How do you know if something is converging?

I don't think there'll be a fine tune card; you'll have the base model vintage whatever year, and then your GPU will do whatever LoRA layers you want it to do; the LoRA will wrangle older dated models into the current of whatever your looking at.

But yeah, for things like programming, if it can do linux and python and some go and sql and javascript, larger domains can be threaded with LORA