It means something, because it an iterative workflow. If you're willing to burn tokens, it's possible for weaker models to implement tasks by incrementally improving drafts.
Pricing at $0.25 and $0.75 already puts its cost well above reasonably reputable inference providers for deepseek v4 flash or qwen 3.8-flash-next or similar class of open weight LLMs that fit in under 170GB of RAM, so I don't see the point. I think this is probably also stupider than laguna s 2.1 which can also be very cheap to serve.
Is it possible to construct a control system where bad, fast and cheap can become good, fast, and cheap through repeated sampling and a strong spec/eval harness?
I am trying to keep an open mind with AI, but I also have little understanding of control theory, trying to learn.
You can, but you need to break the problem into much smaller tasks, then check those answers, and finally have a harness that handles all the context, task breakup, task definitions, and validations each round.
7-8 figures annual spend will buy a hell of a lot of capable local inference hardware you can own, though it won't be at the absurd token/s rate, you'll be able to run almost anything on it... And it'll still have a good residual resale value after 4 years the way things are going now.
Feel like you could spend 6 figures building out a team and the rest renting compute for a whole year, and get the team to create a local inference solution with that kind of budget…
Please do not try to use gpt-oss-120b over Cerebras. It is broken, screws up tool calls most of the time, forgets to end thinking blocks and has all sorts of other issues. The speed is amazing but it is absolutely not worth it, especially at that quite incredible cost. Think: $5–10/minute levels of cost with a single agent, because Cerebras also offers no cache pricing for input tokens at all.
There is no additional fee for using prompt caching. Input tokens, whether served from the cache or processed fresh, are billed at the standard input token rate for the respective model.
So what he’s saying is correct, there is no separate cache pricing, which by normal standards should be 10% of the cost, which can become exceedingly expensive for anything other than single turn. The way they are stating this is of course strange..
Yea i had some pretty meh results using gpt-oss-120b it in my evals where it should have benefited speed alot but it really under performed what i was expecting.
Lighting my codebase on fire at the speed of light. Like microwaving the spaghetti.
I genuinly only see these speeds being useful for customer service/transactional workflows. Of which much smaller models can do the job (but those dont make tons of money for companies like Cerebras that need to pay off massive amounts of debt).
Nobody needs to code at 600 words per second. Using a 100tps model for an hour or so will leave you with 4-8hrs of code review and revision work.
Human code review? What is this, 2025? The modality today is write with one LLM, review by a different one, (important: two different model families will catch errors one series won't) then deploy right to production.
And if you tell the reviewer the author is a competitors model it becomes extra snarky and vigilant. Then give the review results to the author and tell it it's from the competition and it will also become slightly outraged.
No you dont need to code at 600 w/s BUT at those speeds, you can start doing things like asking multiple different agents the same question and picking the best solution each time without noticing the lag.
You can't think that a small startup versus Anthropic's training setup is anywhere near the same scale to make apples to apples comparisons.
Not sure how the Chinese labs pull it off though using autoregressive models. The secret sauce is probably going to be in the training data.
The main reason Google hasn't switched over to DiffusionGemma is because serving at larger batch sizes loses the speed gains you get from diffusion, and most of the primary use case is serving many users at once off a single device with a large batch size.
If you were to move to on-device low latency... like say in a robot or something, then the story might be different...
Personally, I think it's more that text diffusion is not the ideal driver of an agentic work loop than that text diffusion is a total dead end. I am still hoping to see how it does on authoring and editing with further scaling and optimization. I think the push for AGI has put a bit too much focus on the idea of one general model doing everything.
From what I’ve heard, the issue is more that it’s harder to efficiently share the hardware across diffusion requests, so it’s more expensive to serve.
It sounds like there might be opportunities for local models (not open weight, but actually locally run) to use diffusion for faster responses on weaker hardware that doesn’t need to be shared.
But yea, it’s still a red-ish flag that big labs haven’t invested much in it. I could see Google/Apple getting value of this sort of local model, but maybe there’s enough research behind traditional models that it’s not worth the distraction at this point in time.
At some point the bottleneck becomes tool calling.. and as such, it's preferably if the model is co-hosted (in the same datacenter, at least) with your code repository and all other reference/context it needs (full documentation for most ecosystems, maybe even a copy of common crawl to minimize web fetch usage, etc)
I used this a few days ago and thought something must be wrong with how fast it was responding. "Mercury 2.5 is below average in intelligence, but well priced when comparing to other models of similar price." this is so funny. So when you have a stupid model that is fast - what do you use it for?
I have tried using Mercury 2.5 for a lot of my tasks.. but this model just isn't there. It seems to be on par with any 14B model at max. Even GPT-OSS-20B performs way better than this in my own attempts to use it.
I really really wanted to use this because it offers incredible speeds and pricing combinations. But nop.. I still am not using it.. not even for basic tasks.
I'm still sad that we haven't seen a new Taalas style chip a la https://chatjimmy.ai/. Smaller models are good enough now to make that insane burst of tokens so useful.
I don't know the model behind this, but it is absurdly bad.
Write me a coherent paragraph in French, without ever using the letter "e".
Voilà une phrase claire et concise : "Le village est situé dans les montagnes. Le soleil est haut. Il y a des animaux dans le village. Il pleut dans les montagnes."
I suppose this is just a demo of how fast an LLM can be, I wonder if there are tradeoffs with larger/smarter models. Also, for a human usage, at what point are tokens generated fast enough that it's pretty much instant? My bet is below 1000 tps
for comparison, Qwen3.8-Flash-Next only requires 6B parameters for computation, but stores 125B, 51B of those can be comfortably offloaded as they're not actively used in decode but a single token look up.
The Quant iQ4 of this model loads, then, in ~60GB of vram, and on disk it's 85GB.
So if you could etch it, you'd need a ~25GB ssd chip and 60GB of vram.
The vram costs likely contributed to these things being out of reach of the current economic cycle.
The speed means absolutely nothing when it is finishing almost dead last when compared to the frontier AI companies.
Ah, the old "good, fast, or cheap; pick two" proves true once again.
Give it a few months.
Some of us want fast food
Not if your use case needs speed. For one of my products I can't use an LLM that has a p99 of >700ms for TTFT.
If it could output 1k tokens per second but needed 4 seconds to produce the first batch of 4k, would that not be viable?
It means something, because it an iterative workflow. If you're willing to burn tokens, it's possible for weaker models to implement tasks by incrementally improving drafts.
Pricing at $0.25 and $0.75 already puts its cost well above reasonably reputable inference providers for deepseek v4 flash or qwen 3.8-flash-next or similar class of open weight LLMs that fit in under 170GB of RAM, so I don't see the point. I think this is probably also stupider than laguna s 2.1 which can also be very cheap to serve.
The point is the speed.
If the model provides me with bad results because it's dumb, I don't care how quickly it does it.
Is it possible to construct a control system where bad, fast and cheap can become good, fast, and cheap through repeated sampling and a strong spec/eval harness?
I am trying to keep an open mind with AI, but I also have little understanding of control theory, trying to learn.
Smaller models seems to get stuck in "loops" when you try to "handle" them this way.
You can, but you need to break the problem into much smaller tasks, then check those answers, and finally have a harness that handles all the context, task breakup, task definitions, and validations each round.
But there are lots of use cases where a relatively "dumb" model is good enough.
If you care about speed Cerebras gpt-oss-120b is 1400tk/s and "just as smart" in ranking.
I've used it on a few for fun projects and its decent but the speed is crazy to watch.
Also kimi 2.6 at 1000tps (as of may), though when we reached out they had a >12 month waitlist and minimum 7-8 figure annual token spend.
[0] https://www.cerebras.ai/blog/cerebras-kimi-k2-Enterprise
yeah. K2.6 can run on insane speeds. So sad that they don't have K3 yet.
But it can apparently also run 5.6 Sol
Yeah but at “call to discuss pricing” rates
7-8 figures annual spend will buy a hell of a lot of capable local inference hardware you can own, though it won't be at the absurd token/s rate, you'll be able to run almost anything on it... And it'll still have a good residual resale value after 4 years the way things are going now.
Feel like you could spend 6 figures building out a team and the rest renting compute for a whole year, and get the team to create a local inference solution with that kind of budget…
Or better: Qwen 2.8 27b
Unfortunately, the lack of an input cache discount makes it prohibitively expensive for most use cases that aren't one-shot prompts.
well same applies to GPT OSS 120. Qwen is just the much smarter model of the 2 public options on Cerebras.
Please do not try to use gpt-oss-120b over Cerebras. It is broken, screws up tool calls most of the time, forgets to end thinking blocks and has all sorts of other issues. The speed is amazing but it is absolutely not worth it, especially at that quite incredible cost. Think: $5–10/minute levels of cost with a single agent, because Cerebras also offers no cache pricing for input tokens at all.
Which is wild because it does, in fact, do caching
So what he’s saying is correct, there is no separate cache pricing, which by normal standards should be 10% of the cost, which can become exceedingly expensive for anything other than single turn. The way they are stating this is of course strange..
Yea i had some pretty meh results using gpt-oss-120b it in my evals where it should have benefited speed alot but it really under performed what i was expecting.
It was better when they had gemma at 1k. Inco does DS flash at about 600. A few places will do K3 and GLM in the hundreds.
Such a tiny model at that t/s is less impressive than it would have been four months ago.
Lighting my codebase on fire at the speed of light. Like microwaving the spaghetti.
I genuinly only see these speeds being useful for customer service/transactional workflows. Of which much smaller models can do the job (but those dont make tons of money for companies like Cerebras that need to pay off massive amounts of debt).
Nobody needs to code at 600 words per second. Using a 100tps model for an hour or so will leave you with 4-8hrs of code review and revision work.
Human code review? What is this, 2025? The modality today is write with one LLM, review by a different one, (important: two different model families will catch errors one series won't) then deploy right to production.
And if you tell the reviewer the author is a competitors model it becomes extra snarky and vigilant. Then give the review results to the author and tell it it's from the competition and it will also become slightly outraged.
No you dont need to code at 600 w/s BUT at those speeds, you can start doing things like asking multiple different agents the same question and picking the best solution each time without noticing the lag.
I do. I used to use haiku for the speed. Now its just as slow as the rest. Speed is my #1 ranking of how good a model is
[delayed]
if you want to feel the speed without the burn
https://kamilstanuch.github.io/LLM-token-generation-simulato...
I felt happy I could run it at 100tk/s on my new Mac Studio :')
I honestly think the diffusion LLM approach is a dead end
It's telling that frontier labs like Google toyed around with it but didn't invest further even for their most speed and cost sensitive small models
Still unclear for what, if any use cases this is pareto frontier
You can't think that a small startup versus Anthropic's training setup is anywhere near the same scale to make apples to apples comparisons.
Not sure how the Chinese labs pull it off though using autoregressive models. The secret sauce is probably going to be in the training data.
The main reason Google hasn't switched over to DiffusionGemma is because serving at larger batch sizes loses the speed gains you get from diffusion, and most of the primary use case is serving many users at once off a single device with a large batch size.
If you were to move to on-device low latency... like say in a robot or something, then the story might be different...
Personally, I think it's more that text diffusion is not the ideal driver of an agentic work loop than that text diffusion is a total dead end. I am still hoping to see how it does on authoring and editing with further scaling and optimization. I think the push for AGI has put a bit too much focus on the idea of one general model doing everything.
DiffusionGemma was released alongside the other gemma-4 models just a few months ago, so clearly google hasn't abandoned the idea.
K2-Horizon-7B has a diffusion and non-diffusion variant, and they claim the same level of intelligence from both models.
From what I’ve heard, the issue is more that it’s harder to efficiently share the hardware across diffusion requests, so it’s more expensive to serve.
It sounds like there might be opportunities for local models (not open weight, but actually locally run) to use diffusion for faster responses on weaker hardware that doesn’t need to be shared.
But yea, it’s still a red-ish flag that big labs haven’t invested much in it. I could see Google/Apple getting value of this sort of local model, but maybe there’s enough research behind traditional models that it’s not worth the distraction at this point in time.
it's stupid fast!
It's stupid and it's fast.
Couldn't have said it better.
but can it be said in fewer tokens than GGP commenter?
this feels like "we got the same benches as gpt-oss-120b but are also potentially slower while saying it is great"
Well priced when compared to other models of similar price, eh?
Are we allowed to call this slop, even if the output is not directly from an LLM?
The LLMs learned it somewhere...
At some point the bottleneck becomes tool calling.. and as such, it's preferably if the model is co-hosted (in the same datacenter, at least) with your code repository and all other reference/context it needs (full documentation for most ecosystems, maybe even a copy of common crawl to minimize web fetch usage, etc)
I used this a few days ago and thought something must be wrong with how fast it was responding. "Mercury 2.5 is below average in intelligence, but well priced when comparing to other models of similar price." this is so funny. So when you have a stupid model that is fast - what do you use it for?
I have tried using Mercury 2.5 for a lot of my tasks.. but this model just isn't there. It seems to be on par with any 14B model at max. Even GPT-OSS-20B performs way better than this in my own attempts to use it.
I really really wanted to use this because it offers incredible speeds and pricing combinations. But nop.. I still am not using it.. not even for basic tasks.
Try Celeris-magnus-1. We get similar speeds and it’s much closer to qwen 27B dense models.
Didn't knew about this. Thanks.. but, according artificialanalysis.ai, it's intelligence is just about like a 14B model (mistral 3 14b)
depends on your use case I think. for small, transactional tasks it is quite strong and fast.
Cerebras is fast…?
Yep it's fast alright. I wonder how much faster Mercury would become if hosted on Cerebras.
Chat Jimmy clocks at 17K tokens per sec burning LLM into the Chip - https://chatjimmy.ai/ - Source: https://theashishmaurya.medium.com/taalas-the-startup-that-p...
Would have made a nice local tool.
I'm still sad that we haven't seen a new Taalas style chip a la https://chatjimmy.ai/. Smaller models are good enough now to make that insane burst of tokens so useful.
Pretty sure context is in SRAM, and then you have that as a blocker for tasks.
way less of a blocker these days due to sparse attention...
I don't know the model behind this, but it is absurdly bad.
I suppose this is just a demo of how fast an LLM can be, I wonder if there are tradeoffs with larger/smarter models. Also, for a human usage, at what point are tokens generated fast enough that it's pretty much instant? My bet is below 1000 tps
Its Llama 3.1 8B, a very old/small model.
for comparison, Qwen3.8-Flash-Next only requires 6B parameters for computation, but stores 125B, 51B of those can be comfortably offloaded as they're not actively used in decode but a single token look up.
The Quant iQ4 of this model loads, then, in ~60GB of vram, and on disk it's 85GB.
So if you could etch it, you'd need a ~25GB ssd chip and 60GB of vram.
The vram costs likely contributed to these things being out of reach of the current economic cycle.
Then again, good luck writing a coherent paragraph in French without an "e". :-)
A skillful author could craft a full book that way:
https://en.wikipedia.org/wiki/La_Disparition
My 1k parameter RNN is faster