Taalas HC1: 17,000 tokens/sec on Llama 3.1 8B vs Nvidia H200’s 233 tokens/sec. 73x faster at one-tenth the power. Each chip runs ONE model, hardwired into the transistors.
Taalas HC1: 17,000 tokens/sec on Llama 3.1 8B vs Nvidia H200’s 233 tokens/sec. 73x faster at one-tenth the power. Each chip runs ONE model, hardwired into the transistors.
This would be great if you could have a machine that would allow you to swap chips… and then they only charge < 50 USD for each chip.
Can’t be that cheap unfortunately if they maxed out the die area. Though it is an older node so maybe not as expensive as flagship GPU chips and shit
Would be great, but feels unlikely, most of the gains they’re making rely on the lack of versatility.
Go landfills!
That’s all technology though, sadly.
This one feels shorter-lived than the average chip, tho.
With the hardwiring and all.
The thing that differentiates ChatGPT and Claude is likely more the RAG pipeline that backs them and feeds them context. The models really aren’t getting better, we’re just getting better at using them to break tasks down into units so small AI can figure it out. I’d bet a GPT 5 model or a Claude Opus 4.6 model would last 5, maybe 10 years before you really start to notice its capabilities are falling behind. I’ll bet you could use GPT 4o for 5-10 years and it would be fine.
But if they could make it so the chip is the only thing that is obsolete, That could be recycled pretty easily, or resold.
Then it would stop being 73 times faster than NVIDIA.
That doesn’t make sense.
If you add levels of indirection, extra transistors and such, it would be surprising to manage to maintain the same level of performance, especially since this design seems to rely on hardwiring to achieve its speed…
Pretty sure the advantage is the AI directly on the chip.