61–70 of 712 posts
Imagine the size of chip needed to 'etch' something like Qwen 3.6 27B in size.
> Our second model, still based on Taalas’ first-generation silicon platform (HC1), will be a mid-sized reasoning LLM
Earlier quoted context omitted.
I know it's a relatively tiny model, but damn, is that thing fast. It also mostly passes the "schlong" test https://pastes.io/YcxSi8Fp
I didn't realize there was a SchlongBench™ (but of course there is). What's it test? (asking seriously)
It's also a question I have found will cause models that don't know what it is to go off quickly in a direction of hallucination trying to explain it, so the hallucination is evident very quickly starting from the first ever prompt issued with 0 context fill. Example: I had a model write four detailed supposedly-accurate sounding, grammatically correct paragraphs saying its origin is from AAVE (African American Vernacular English), which it most certainly is not
You could do the same by picking any topic that is very rarely discussed in conversation, some esoteric and narrow piece of knowledge and asking the model about it.
Can anyone comment on the economics and likely turnaround times of this process, when it’s more mature? Would it be realistic for a frontier lab to deploy this or would the turnaround time mean the model is always too out of date? Assuming the weights and architecture are eventually stable, how much cheaper would this end up being?
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"
Can anyone comment on the economics and likely turnaround times of this process, when it’s more mature? Would it be realistic for a frontier lab to deploy this or would the turnaround time mean the model is always too out of date? Assuming the weights and architecture are eventually stable, how much cheaper would this end up being?
At the time people were no doubt saying yes but now 3.8 is out, is that still desirable?
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.
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 'cartrid…
it might already be time to start burning the best small models onto hardware since it's possible they can't get much better at many tasks like knowledge recall due to the inherent information density limits for models at a given size.
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.
Considering the rate of model development and rail hopping, seems like baking models into silicon is speed-running obsolescence.
Imagine the size of chip needed to 'etch' something like Qwen 3.6 27B in size.
If tolerant, they could churn out many cheaper chips, some perhaps with slight abnormal tendencies ;)