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Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling

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Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling

#101

Post by akiselev »

stillpointlab wrote: post #73
LarsDu88 wrote: post #48

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…

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 characters in real time and have the LLM drive the storyline. The devs may be limited to training/RLHFing legacy models, but the performance enables many more use cases.

Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling

#102

Post by throw1234567891 »

LarsDu88 wrote: post #48

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…

https://taalas.com/

Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling

#103

Post by lend000 »

gopalv wrote: post #86
LarsDu88 wrote: post #48

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…

> Does anyone think we need a Mythos level model to plan a road trip, or give someone tips on making a cake recipe? This is starting to look at a lot like Intel vs Arm from the last era. The Fable & Mythos are starting to look like a giant Xeon, while the smaller lighter models are starting to look like a lot of tiny ARM chips which sip on power instead. The risk is the same as what Intel had. There is a group who ar…

> The risk is the same as what Intel had. There is a group who are pushing them to go bigger and with a resource no limit approach, who have a lot of dollars to push you that way. Follow them and they lead you to a pile of money, but then you risk something like Apple Silicon happening.

The problem was that Intel stopped making things faster and better, and shifted to wearables and mobile chips instead of investing in their hard-tech strategy that had worked for decades.

Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling

#104

Post by JSR_FDED »

bko wrote: post #5

I think the risk is overstated. For one, on the margin people are willing to pay a lot for slightly better models. I know personally the value the LLM adds to my workflow is considerably more than the $200/m I pay the frontier labs. I have no interest in optimizing that to get it slightly lower. There are a very vocal minority that optimizes this or companies whose LLM expense is marginal, but I think that's the mino…

Spending a lot on making your top developers more effective is easy to justify.

But once you have an AI-powered application in production, why wouldn't you go with far cheaper and capable enough models? That's just optimising a business process like any other. You'd use cheaper providers whether Chinese, or other on-prem models.

Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling

#105

Post by cmrdporcupine »

uncivilized wrote: post #98
cmrdporcupine wrote: post #15

Earlier quoted context omitted.

For people cranking out SaaS / web services / web pages / "full stack" work I don't see a huge difference. If you're building an optimizing compiler, a CUDA kernel, a database, a high performance concurrent data structure with tricky locking, etc. etc. it's still not really close. Sol 5.6 on high just slays e.g. GLM 5.2 for this kind of work for me. I'm sure K3 is fine for these things too, but I can't afford it at i…

Regarding the latter, are you able to obtain good results from an LLM? With graphics programming work, I find LLMs only help in cases where the task would take me 5 mins or so.

Absolutely.

Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling

#106

Post by 535188B17C93743 »

misiti3780 wrote: post #56
LarsDu88 wrote: post #48

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…

is anyone doing this ?

Yeah, I've heard of Taalas doing it. Not sure of others but I'm sure lots of companies are considering it, especially as we start to hit points of depreciating returns in training.

Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling

#107

Post by wongarsu »

LarsDu88 wrote: post #48

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…

While running the model at 9000 tokens/s is the more flashy demo, I imagine running 1000 concurrent requests at 150 tokens/s each is the much more achievable goal

Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling

#108

Post by LarsDu88 »

stillpointlab wrote: post #73
LarsDu88 wrote: post #48

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…

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…

Honestly, whether you think burning current SOTA to hardware is an overinvestment risk depends on what your definition of intelligence is. If you think intelligence is something that can grow like height such that 18 months from now we will basically be bowing down to machine god giants that are running on B200s, then investing in ASICs is the wrong move. However, if you you subscribe to the (very reasonable view) that intelligence is more like a round ball that we are trying to make as spherical as possible (ala Francois Chollet's writings), then at some point the ball will be smooth enough for most people and many tasks.

It takes about 18 months to go through the design, verification, and manufacturing process if you move at breakneck pace. Design could probably be sped up.

About 18 months ago the top model was GPT-4o. Not great by today's standards, but still good enough for many tasks (certainly a big chunk of chatbot queries). The current SOTA covers far more use cases, but importantly at a level that surpasses many thresholds of utility.

Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling

#109

Post by spaceman_2020 »

Dario can reverse this by going on the podcast circuit again and threatening everyone with 75% job losses due to (his) AI this time

Ramp the number up to 85% if that doesn’t work

If it still doesn’t work, go nuclear and target 100% job losses language

Re: Kimi K3, Qwen 3.8, and Anthropic's (Potential) Unravelling

#110

Post by LarsDu88 »

mountainriver wrote: post #85
LarsDu88 wrote: post #48

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…

Sol is running on Cerebras right?

Cerebras is not an ASIC. It is a large wafer scale chip that has a load of small SRAM modules paired with tiny compute models. The SRAM is basically a giant cache that is supposed to eliminate the bottleneck between shuttling and materializing big tensors between DRAM and cache, but the actual amount of SRAM is still nowhere near enough to server a frontier model on a single wafer. You still need dozens of wafers, which makes Cerebras rather cost intensive given that Nvidia can get dozens of GPUs out of a single wafer
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