This is not a general purpose chip but specialized for high speed, low latency inference with small context. But it is potentially a lot cheaper than Nvidia for those purposes.
Tech summary:
- 15k tok/sec on 8B dense 3bit quant (llama 3.1)
- limited KV cache
- 880mm^2 die, TSMC 6nm, 53B transistors
- presumably 200W per chip
- 20x cheaper to produce
- 10x less energy per token for inference
- max context size: flexible
- mid-sized thinking model upcoming this spring on same hardware
- next hardware supposed to be FP4
- a frontier LLM planned within twelve months
This is all from their website, I am not affiliated. The founders have 25 years of career across AMD, Nvidia and others, $200M VC so far.
Certainly interesting for very low latency applications which need < 10k tokens context. If they deliver in spring, they will likely be flooded with VC money.
Not exactly a competitor for Nvidia but probably for 5-10% of the market.
Back of napkin, the cost for 1mm^2 of 6nm wafer is ~$0.20. So 1B parameters need about $20 of die. The larger the die size, the lower the yield. Supposedly the inference speed remains almost the same with larger models.
Interview with the founders:
https://www.nextplatform.com/2026/02/19/taalas-etches-ai-mod...
Tech summary:
This is all from their website, I am not affiliated. The founders have 25 years of career across AMD, Nvidia and others, $200M VC so far.Certainly interesting for very low latency applications which need < 10k tokens context. If they deliver in spring, they will likely be flooded with VC money.
Not exactly a competitor for Nvidia but probably for 5-10% of the market.
Back of napkin, the cost for 1mm^2 of 6nm wafer is ~$0.20. So 1B parameters need about $20 of die. The larger the die size, the lower the yield. Supposedly the inference speed remains almost the same with larger models.
Interview with the founders: https://www.nextplatform.com/2026/02/19/taalas-etches-ai-mod...