The open weight, open architecture releases of the past several days has me more convinced that ultimately, the winner will be whoever burns their models to ASICs fastest. The LLMs themselves are capable of doing some aspects of chip design as evinced by the K3 press release. Furthermore, the frontier models are "good enough" for a wide swathe of tasks and will soon hit that threshold for a good amount of software en…
The field is moving too fast for it to make sense. ASICs have a 12-24 month development cycle and you’d have to throw them in the trash 4 months later.
Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling
251–260 of 349 posts
Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling
#252Earlier quoted context omitted.
I'd imagine it has to be immense. Most of the engineers I know are spending $200/day, let's say 20 workdays per month, $4000/mo. Whatever % of revenue it is, it's got to be close to 100% of profit.
Who is paying $200/day? My company has me on a $30/month plan (I assume they pay annually?). I use Claude constantly and only hit usage limits when I try to do 4+ projects at once. I can't imagine why anyone would need almost 7x that.
But $200/day is very common in the business. The monthly plans are just a trial version of what's going to come for all of us. That's why I am very carefully looking into the open models today.
Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling
#253The open weight, open architecture releases of the past several days has me more convinced that ultimately, the winner will be whoever burns their models to ASICs fastest. The LLMs themselves are capable of doing some aspects of chip design as evinced by the K3 press release. Furthermore, the frontier models are "good enough" for a wide swathe of tasks and will soon hit that threshold for a good amount of software en…
Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling
#254In November of 2025, I would have said that Anthropic's Opus 4.5 model together with their Claude Code harness was the first and only system where a well-specified software feature could be implemented correctly for me in one shot. Today, I'm about equally happy to use Claude or Codex. And if both of those start to squeeze customers for money or get too zealous about safety it looks like there are going to be plenty…
Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling
#255The open weight, open architecture releases of the past several days has me more convinced that ultimately, the winner will be whoever burns their models to ASICs fastest. The LLMs themselves are capable of doing some aspects of chip design as evinced by the K3 press release. Furthermore, the frontier models are "good enough" for a wide swathe of tasks and will soon hit that threshold for a good amount of software en…
A Fable 5 model running at 9,000 tokens/s on an ASIC rather than 150 tokens/s on electricity chugging Nvidia GPUs, or even giant SRAM Cerebras or Groq chips could be good enough to meet the majority of demand. 640K ought to be enough for anybody.
I get this reference!
Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling
#256Earlier quoted context omitted.
I'm not convinced, mostly because things like crypto, which I believe went into ASICs, were based on very slowly moving and mostly understood algorithms. LLMs and model architectures seems significantly more volatile. I wouldn't want to be working out the finer details of my chip rollout only to find a new paper/approach that give multiples of performance. So I guess it depends on how much the latest-greatest model m…
The churn is an issue. For a while there, it felt like we were getting a new number format every month or two (e.g., fp4, ternary, etc.). That level of innovation works against moving things into hardware, or at least you need to be willing to spin hardware constantly.
Assuming such work was happening.
Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling
#257Earlier quoted context omitted.
I'm not convinced, mostly because things like crypto, which I believe went into ASICs, were based on very slowly moving and mostly understood algorithms. LLMs and model architectures seems significantly more volatile. I wouldn't want to be working out the finer details of my chip rollout only to find a new paper/approach that give multiples of performance. So I guess it depends on how much the latest-greatest model m…
The tokens per second performance numbers coming from Cerebrus/Talas are several orders of magnitude higher than models running on GPUs, which is such a huge step change that it will enable many more uses of LLMs that are impractical otherwise. I.e. think about gamers and burning in an LLM chip on a game console like a future Play Station - it doesn't matter if its a frontier LLM if it allows them to talk to in game…
Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling
#258Earlier quoted context omitted.
Open-weight models were lagging 4 months behind OpenAI/Anthropic at the beginning of the year. They are now just 4-6 weeks behind.
Kimi K3 is still worse than Fable and Fable was trained >4 months ago.
Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling
#259Earlier quoted context omitted.
Interesting. Is the speedup from specializing for the shape of Llama 3.1 or are they (contra my mental model) actually winning on burning in the weights?
The weights are in SRAM, so the LLM architecture is burned in but the weights can be updated.
If they designed this right, it means that once they have a model, so long as they keep the hyperparameters fixed they can change the weights much faster than it takes to spin up a completely new chip, essentially at a cost of doing a minor revision.