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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

#131

Post by thisisit »

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…

This is a bad take.

> Think about it another way. Consider how much money Microsoft spends on maintaining Excel. There are open source alternatives that have >90% of the functionality, they'll even work w/ Excel files and generate them.

Sure but were these open source alternative as pervasive and widespread and talked about when Microsoft was bundling Excel with the OS? Or were there millions of dollars, if not billions of dollars being spent by these competitors? The answer is No. It is not the same scenario.

While I understand the logic of harnesses - those are not full proof. It is trivial to setup K3 or Qwen to work with Claude Code/Codex by intercepting requests and routing to K3/Qwen. You have tools like CCSwitch which can do that for you.

The only thing remains is - people willing to pay a lot for slightly better model. That is true but price sensitivity is also a thing. The mania case for Anthropic/OpenAI/SpaceX/Google is that they will capture large part of the enterprise market. But in many cases outside coding AI capabilities will be resold.

That is instead of buying AI for lets say HR functions you pay for embedded AI in your HR software. And the HR software company will have incentives to raise their own margins. If their provide similar looking experience using Qwen/K3 why will they buy Anthropic/OpenAI. That is the real risk here. While as an individual you can continue to pay top dollar.

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

#132

Post by yalogin »

The bigger question for me is , at what point does investing in higher capability general models will stop showing the ROI?

For example - What percentage of workflows require this new highly capable model? How much of it can be replaced with the software tooling around it? What I mean is if the software tooling can optimize the query over a few iterations does it get the same output as from a single shot high capability model query?

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

#134

Post by hughw »

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…

  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.

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

#135

Post by BurnerOptical »

quotemstr wrote: post #116
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…

What precisely is an ASIC supposed to do that a more programmable accelerator can't? Memory latency is memory latency: doesn't matter whether it's embedded in a cache-line wait state or some flipflop state machine. ROM isn't going to be faster than RAM either. Likewise, for compute, is the ASIC somehow going to beat a systolic array? You can't have one circuit per weight: the die area and electrical fan-out would be…

[dead]

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

#136

Post by alex43578 »

stillpointlab wrote: post #126
alex43578 wrote: post #90

Earlier quoted context omitted.

I think the gamble comes down to how many tokens need to be served on your best model, versus how many can be served in the cheapest/fastest way. Imagine if Anthropic could give effectively unlimited access to Sonnet, for $20. Wouldn’t that be an appealing option for many users? I know I’d make a lot of use of it for agentic tasks, office work, summarization, etc; when right now I’d save quota for more important task…

I mean, if I imagine Anthropic giving away unlimited Sonnet 4.5 away at $20, I would still be paying the $200 for fable. It is a bit like saying "why would you hire someone with a doctorate when you could get unlimited high school grads". How appealing that sounds depends on your needs.

It’s like how most people on here would want a loaded MacBook Pro or RTX 5090, but chromebooks and iGPUs do volume.

There’s absolutely a place for a lifestyle subscription to a sonnet model that you could just use everywhere all the time.

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

#137

Post by JohnBooty »

LarsDu88 wrote: post #108
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…

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) th…

At a 50-100x speedup even a GPT-4o class model could perhaps compete with much newer models simply by thinking deeper, doing harness-controlled Ralph loops, etc. Sure, then it might be "only" ~2-5x faster, but, you wouldn't need to throw all the ASICs into the trash bin.

One could also imagine hybrid models, where part of the model is burned into ASICs and part of the model exists in VRAM/HBM2 so it can be updated.

I don't have enough low-level knowledge to evaluate the technical or economic feasibility of the above ideas, however.

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

#138

Post by applicative »

hadlock wrote: post #125
alex43578 wrote: post #94

Earlier quoted context omitted.

Except aren’t these Chinese models having to go big too? While they might not be Mythos/Fable sized, a trillion plus parameters is hardly a local server or even desktop to mainframe style jump

I've had 2b models give a plausible Paris vacation itinerary. A tools-capable 12b and especially 30b model from 2026 is certainly capable of producing passable results. I was demonstrating the qwen 3.6 27b model I stood up last week to my wife and it gave her a passable Moroccan Chicken recipe. With tool calling (search) they're quite good.

It’s AI talking about AI so cum grano salis, but my AI is saying I would need at least a half million dollar in hardware to run the newer high quality Chinese models with bemchmark-competitive force.

You can run a Moroccan chicken fragment on the cheap in homage to what you cannot run

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

#139

Post by stillpointlab »

akiselev wrote: post #101
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…

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, we'll have to wait and see. It's a lot of silicon to dedicated to a static non-changing thing. My money would be on programmable TPU-like things (Apple's NPU kind of stuff). It just seems more flexible to have an array of compute that you can load different models into, so you can update it, etc.

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

#140

Post by margalabargala »

stillpointlab wrote: post #126
alex43578 wrote: post #90

Earlier quoted context omitted.

I think the gamble comes down to how many tokens need to be served on your best model, versus how many can be served in the cheapest/fastest way. Imagine if Anthropic could give effectively unlimited access to Sonnet, for $20. Wouldn’t that be an appealing option for many users? I know I’d make a lot of use of it for agentic tasks, office work, summarization, etc; when right now I’d save quota for more important task…

I mean, if I imagine Anthropic giving away unlimited Sonnet 4.5 away at $20, I would still be paying the $200 for fable. It is a bit like saying "why would you hire someone with a doctorate when you could get unlimited high school grads". How appealing that sounds depends on your needs.

Right, and while there are needs that require a doctorate, having unlimited high school grads would be immensely useful for many many tasks.

The ability levels of the cheap models are encroaching on the abilities of the frontier models faster than frontier models are expanding their abilities. If we haven't already, we will very soon reach a "good enough" state where having the "best" model matters less and less and less.

By analogy, if you buy a new computer, do you get the absolute fastest CPU available? Maybe, depending on your workload. But if you're 90% of the population, you get the cheapest one that has enough power to meet your expected workload, which is mid-range, not top of the line.

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