Really hoped to see their hw out in the wild one day

Didn't even give them a chance to launch the hardware.

While this design is self-limiting I think its a good approach. It doesn't take an entirely new architecture or infinite memory to produce significant performance improvement.

Well so much for that dream.

Guess we can look forward to picking these up ex-enterprise on ebay for under $5k a pop in a decade or two

The demo: https://chatjimmy.ai/

This is probably a win-win. The team gets paid, and we get greater assurance that their best ideas and architectures -- which are truly impressive -- are going to see the light of day in actual products.

so qwen3.x-27b on hardware? or better deepseek-v4-flash on hardware .

Honestly, this is starting to make more and more sense. SOTA models are starting to converge to certain architecture and capabilities. I wouldn’t be surprised we end up with a base model ASIC + “fine tune” card where it’s a physical LoRA style adapter.

Toronto Canada startup btw.

AMD could have saved their money and used their own hardware! I've got a language model doing 60k tok/s on AMD hardware already, a Xilinx Kria K26 SOM, with the weights baked into URAM/BRAM with zero DRAM in the token loop. Same thesis as Taalas: single-stream decode is bandwidth bound, so stop fetching weights from far away.

Caveats stacked high, obviously. It's 3.16M parameters (tinystories, and I also have a kevin-speak lemmatised version), the tokens are characters, and the 60k record is 16 streams that each remember exactly one token of context, so it's blisteringly fast at saying nothing. The honest build with full context and KV caching still does ~19k tok/s on one stream though.

I keep messing with the blogpost with the live demo, but I'm planning on flipping it to live in the next day or two

I feel like NAND process tech could become useful at solving some of these problems. A GPU where you can update the weights a few thousand times may be sufficient.

Token quantity will have a quality all its own.

I'm surprised neither OpenAI nor Anthropic made this move first. The Chinese open weight models are pulling ahead and commoditizing their value proposition.

Baking models onto silicon would've been the next logical move to get a moat.

Google is already doing this and has an experimental project on top of already having TPUs and cramming their quantized flash onto individual TPUs for inference.

Imagine the size of chip needed to 'etch' something like Qwen 3.6 27B in size.