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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

#291

Post by daadx »

simianwords wrote: post #74
drakythe wrote: post #64

Earlier quoted context omitted.

Sunk cost fallacy. So much money has been invested in Anthropic and OpenAI at this point that to declare it a loss and walk away could potentially destroy a lot of VC firms, and a non-trivial chunk of the US Economy.

Sunk cost fallacy is not relevant to future investment.

what an absolute plonker

do you know how ROIC is calculated?

Good luck hiding your huge sunk cost of bilions and billions in there bro. I wish people who had zero understanding of actual finance would never comment about it.

Moreover if they declare their existing assets are bunk, the valuation is marked down, especially after now pricing in failure risk - this is catastrophic for VC's.

Again, bro, just be quiet.

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

#292

Post by teleforce »

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…

>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.

Yesterday there's a news on a breakthrough for probabilistic computer with 1 million p-bits [1].

Since LLM is stochastic in nature, this type of new computer can be much better than ASIC for processing LLM data.

[1] Biggest Probabilistic Computer Turns Noise into Answers:

https://news.ycombinator.com/item?id=48971938

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

#293

Post by hadlock »

alex43578 wrote: post #233
vitally3643 wrote: post #180

Earlier quoted context omitted.

You're missing the point. You very rarely need the biggest and "best" model. This is psychology and nothing more, people always want the "best" and don't often consider "good enough". Small models are good enough depending on your task. That's the point. A model you can run on your phone or laptop is an incredibly useful tool for a lot of problems even though it isn't the "best" theoretically possible model.

You're missing the point: those small "good enough" models aren't monetizable and haven't been for months already. All of the value in LLMs is going to come from frontier models at a high cost to businesses/governments. It'll be the difference between next day air-mail of a contract and sticking a stamp on your christmas card to Grandma - nobody's making a profit on the christmas card.

I suspect in 5 years everyone will have the equivalent of a 512gb mac mini running a 500b class open weight model for 98% of their tasks, shelling out to openai/anthropic for the other 2%. People who need higher end models will own the equivalent of 4 x 512gb mac mini running a 2.8T class open weight model. I don't know where OpenAI and Anthropic are going to get their revenue from to keep developing SOTA frontier models at that point.

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

#294

Post by daadx »

InsideOutSanta wrote: post #27
behnamoh wrote: post #9

Nope, I'll still buy Claude because the overall XP is better than Kimi and Qwen who literally copied basic harnesses to make kimi-cli and qwen-cli, respectively. Also, you can tell if a model is genuinely powerful and well-thought-out vs a model that acts like it. It's like Apple vs Xiaomi/Huawei. Sure, you can get a Huawei with bells and whistles, but most people learnt the hard way that those companies just copy th…

As somebody who bought an iPhone the day it became available and has been using iPhones for years, my favorite phone I've ever owned is my Huawei Mate XT Ultimate. People keep pretending that Chinese companies only make second-class copies of American products until it's too late.

people buy Apple because of the brand - in particular trust.

the vast majority of people do not care about like-for-like phones based purely on features.

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

#295

Post by nightski »

teleforce wrote: post #292
stillpointlab wrote: post #73

Earlier 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…

>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. Yesterday there's a news on a breakthrough for probabilistic computer with 1 million p-bits [1]. Since LLM is stochastic in nature, this type of new computer can be much better than ASIC for processing LLM data. [1] Biggest Probabilistic Computer Turns Noise into A…

Except LLMs are not stochastic in nature. Correct me if I am wrong, but it's just the final layer which outputs a distribution across the output tokens. In reality, the rest of the model is deterministic.

This is not like a bayes model or something were it's distributions all the way down.

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

#296

Post by breuleux »

stillpointlab wrote: post #139
akiselev wrote: post #101

Earlier quoted context omitted.

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…

I admit I would like a faster model - but even though I have faster models available I still go to Fable or GPT-5.6 90% of the time. So there is a gap between a potential preference and a revealed preference. Custom AI for things like facial recognition in cameras has existed for decades, before LLMs were a thing. I don't see that getting replaced. And on-device conversational intelligence might go that route as well…

> even though I have faster models available I still go to Fable or GPT-5.6 90% of the time

What about all the things you don't currently use an LLM for?

If a specialized chip can run a model 100 times faster, you can suddenly use it for a lot of things at sub-second latency. You can write "make white transparent and add a red outline to x.png" instead of the corresponding imagemagick invocation and perceive little to no latency difference. You can hook it up to your browser and have it yank out all advertisements live, or tell it to highlight anything that might interest you, again, live. There's probably thousands of latent use cases nobody has thought of that would be enabled by a truly fast LLM, even a mediocre one.

I don't think an on-device model needs to change much; it's already quite general in its capabilities.

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

#297

Post by Havoc »

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…

Fun as the asic play would be it has a giant hole - context storage. Raw speed only gets you so far if you can’t store and cache

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

#299

Post by stingraycharles »

port3000 wrote: post #50

It's amazing how quickly Fable went from 'Game-changing model that needs to be banned' to 'Yeah it's alright, but OpenAI is also just as good and there are a couple of good open weight alternatives that are equivalent for almost everything' The hype cycles are shortening, perhaps we really are reaching some kind of plateau this time (famous last words)

Fable is still the same model, it’s still a great model, and to be honest all these articles writing and speculating on how the LLM industry is going to evolve are not that insightful nor interesting.

I don’t think one should pay much attention to them.

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

#300

Post by prirun »

breuleux wrote: post #296
stillpointlab wrote: post #139

Earlier quoted context omitted.

I admit I would like a faster model - but even though I have faster models available I still go to Fable or GPT-5.6 90% of the time. So there is a gap between a potential preference and a revealed preference. Custom AI for things like facial recognition in cameras has existed for decades, before LLMs were a thing. I don't see that getting replaced. And on-device conversational intelligence might go that route as well…

> even though I have faster models available I still go to Fable or GPT-5.6 90% of the time What about all the things you don't currently use an LLM for? If a specialized chip can run a model 100 times faster, you can suddenly use it for a lot of things at sub-second latency. You can write "make white transparent and add a red outline to x.png" instead of the corresponding imagemagick invocation and perceive little t…

> hook it up to your browser and have it yank out all advertisements live

AI: "I'm sorry, a security guardrail prevents me from performing this operation."

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