5 ms·

(Where did you see that?) This was also interesting: "CS-4 delivers more than 1,000 tokens per second on models exceeding 10 trillion parameters." Was it know

(Where did you see that?) This was also interesting: "CS-4 delivers more than 1,000 tokens per second on models exceeding 10 trillion parameters." Was it known that there were 10 trillion parameter models in use? I think the frontier providers keep the size of their models carefully hidden.

ewild 2mo ago
It's rumored fable is around that 10T number
- walrus01 2mo ago
  If this is true, it's even more impressive that some of the open weight models that are <3.5T in size, approx 33% of its size, are within a few points of it in the artificial analysis leaderboard.
  - manquer 2mo ago
    Not necessarily, there could be diminishing returns on mere parameters count . There is nothing to say for example a 1 Quadrillion parameter model will be vastly more intelligent than current SOTA especially since new training data is largely synthetic today
    - HDBaseT 2mo ago
      That's precisely what he is saying, there is diminishing returns (or optimization left on the table).
      - manquer 2mo ago
        I read it as it is impressive because smaller models 2.5T are squeezing similar returns as 10T models despite being 1/4th size not that there beyond 2T today the number or parameters do not have much meaning
        verdverm 2mo ago
        or the latest qwen3.8 27B doing so well at ~1/100 the size of K3
        rnewme 2mo ago
        What about general knowledge you can get out of it before hallucinations start?
        walrus01 2mo ago
        It did OK on schlongbench v1.0 (test of a specific niche word that doesn't make it into smaller LLMs) but it sure does love to count words https://pastes.io/r8F1AY8h https://pastes.io/r8F1AY8h
        verdverm 2mo ago
        I do not rely on any LLM of any size for general knowledge baked into the weights, they all hallucinate and that is the wrong way to hold them imo I think there is some merit in that smaller models cannot memorize so much of the training data, i.e. that they are less likely to do copyright infringement, and by analogy not having memorized SDK / API surfaces that have since changed from the training data
        walrus01 2mo ago
        > I do not rely on any LLM of any size for general knowledge baked into the weights You have to rely on it to a certain level for agentic/coding work, presuming that's the general subject we're talking about here... For instance I recently encountered a project where it would have been a lot worse if the LLM didn't already know "what is" xterm.js and a bunch of its associated npm-related/node related software. If it was still smart but had to google and find results for everything it would have been a lot more time consuming and risked sending it down a wrong path.
        verdverm 2mo ago
  - habosa 2mo ago
    GLM 5.3 is "only" 753B parameters. Much much smaller.
  - mlmonkey 2mo ago
    You want to take a look at the "Scaling Laws" paper, so you can extrapolate from these numbers.
    - stymaar 2mo ago
      This paper, as well as the Chinchilla one, aged like milk though.
  - scosman 2mo ago
    And GLM is only 0.7T! But these labs distill off the larger models. Both officially at the labs with the big ones, and unofficially. We need the giant models to get the smaller models.
- johnnyApplePRNG 2mo ago
  Fable is most definitely nowhere near 10T. The cost to train and infer that would be insane, even by today's standards.
  - nl 2mo ago
    Fable is strongly believed to be around 10T. The most conservative estimate I've seen is 8T. Eg: https://www.reuters.com/technology/bytedance-targets-mega-ai-model-nearing-anthropics-mythos-ft-reports-2026-08-07/ https://www.reuters.com/technology/bytedance-targets-mega-ai... That reports Mythos as 8T and Fable as 5T, but I think they mean Opus as 5T, which is widely known, eg: https://eu.36kr.com/en/p/3760679047267075?ref=explainx https://eu.36kr.com/en/p/3760679047267075?ref=explainx Both Grok and Bytedance are training 10T models.
    - andai 2mo ago
      Wasn't Opus ~1.5T and Fable is about twice that?
    - stymaar 2mo ago
      The fact that Musk claims Opus is 5T to justify why Grok is far behind should be taken with a massive grain of salt given he's a recidivist mythomaniac. Honestly if Opus is 5T parameters while being matched by the biggest open models that are at least twice smaller, it would mean that the US is already behind China in the AI race, despite a significant edge in compute.
      - WinstonSmith84 2mo ago
        Yes. And Opus goes a very long way compared to Fable, Anthropic isn't doing any favour, it's clearly just 2 models with a very different amount of parameters.
      - nl 2mo ago
        The open models don't really match Opus. For example I regularly do Fable+Opus agentic coding runs over 24 hours without intervention. I think I've had GLM do a run that was a few hours. That's the closest I've had an open model come on that kind of work.
alightsoul 2mo ago
pretty sure 10 trillion parameters is now the norm among closed ai labs, given that nvidia also references the same 10 trillion number for their nvl72 racks
- KronisLV 2mo ago
  Pretty bad efficiency then unless that only applies to Fable class but even then - Kimi K3 is around 3T and does similarly well in most benchmarks.
  - esafak 2mo ago
    As others have noted, most benchmarks stress the torso, not the tail.
nl 2mo ago
Mythos/Fable are around 10T: > According to FT, industry estimates say Anthropic's most advanced Mythos 5 has about 8 trillion parameters and Fable 5 about 5 trillion https://www.reuters.com/technology/bytedance-targets-mega-ai-model-nearing-anthropics-mythos-ft-reports-2026-08-07/ https://www.reuters.com/technology/bytedance-targets-mega-ai... I believe this report has confused Opus (which is known to be around 5T) and Fable. Other reports say 10T. See for example https://eu.36kr.com/en/p/3760679047267075?ref=explainx https://eu.36kr.com/en/p/3760679047267075?ref=explainx where Musk talks about the models being trained on Colossus2
- zozbot234 2mo ago
  > I believe this report has confused Opus (which is known to be around 5T) and Fable. 5T for Opus feels quite high though. DeepSeek V4 Pro is a mere 1.6T and often described as a match with Opus in overall quality. Even the largest open models in common use are around 2.8T.
  - Implicated 2mo ago
    > and often described as a match with Opus in overall quality It's not. Idk about who has more T's but, unfortunately, DS4 pro is not a match to Opus, at least not Opus 4.8.
  - nl 2mo ago
    It's not an Opus match. The difference is very visible in long tail applications. Exactly where you'd expect parameter count to matter.
- YmiYugy 2mo ago
  I'm confused. I thought Mythos 5 and Fable 5 were exactly the same model just with a different security layer in front of it. Could they mean the Mythos 5 Preview?
  - nl 2mo ago
    Yes. One reason why I think that report has confused Fable and Opus.
redox99 2mo ago
You don't really need to train a 10T model to test cerebras against a 10T model. You can feed it an untrained (randomly initialized) model and benchmark it. Result will be gibberish but performance the same.

ewild 2mo ago

It's rumored fable is around that 10T number

walrus01 2mo ago
If this is true, it's even more impressive that some of the open weight models that are <3.5T in size, approx 33% of its size, are within a few points of it in the artificial analysis leaderboard.
- manquer 2mo ago
  Not necessarily, there could be diminishing returns on mere parameters count . There is nothing to say for example a 1 Quadrillion parameter model will be vastly more intelligent than current SOTA especially since new training data is largely synthetic today
  - HDBaseT 2mo ago
    That's precisely what he is saying, there is diminishing returns (or optimization left on the table).
    - manquer 2mo ago
      I read it as it is impressive because smaller models 2.5T are squeezing similar returns as 10T models despite being 1/4th size not that there beyond 2T today the number or parameters do not have much meaning
      - verdverm 2mo ago
        or the latest qwen3.8 27B doing so well at ~1/100 the size of K3
        rnewme 2mo ago
        What about general knowledge you can get out of it before hallucinations start?
        walrus01 2mo ago
        It did OK on schlongbench v1.0 (test of a specific niche word that doesn't make it into smaller LLMs) but it sure does love to count words https://pastes.io/r8F1AY8h https://pastes.io/r8F1AY8h
        verdverm 2mo ago
        I do not rely on any LLM of any size for general knowledge baked into the weights, they all hallucinate and that is the wrong way to hold them imo I think there is some merit in that smaller models cannot memorize so much of the training data, i.e. that they are less likely to do copyright infringement, and by analogy not having memorized SDK / API surfaces that have since changed from the training data
        walrus01 2mo ago
        > I do not rely on any LLM of any size for general knowledge baked into the weights You have to rely on it to a certain level for agentic/coding work, presuming that's the general subject we're talking about here... For instance I recently encountered a project where it would have been a lot worse if the LLM didn't already know "what is" xterm.js and a bunch of its associated npm-related/node related software. If it was still smart but had to google and find results for everything it would have been a lot more time consuming and risked sending it down a wrong path.
        verdverm 2mo ago
- habosa 2mo ago
  GLM 5.3 is "only" 753B parameters. Much much smaller.
- mlmonkey 2mo ago
  You want to take a look at the "Scaling Laws" paper, so you can extrapolate from these numbers.
  - stymaar 2mo ago
    This paper, as well as the Chinchilla one, aged like milk though.
- scosman 2mo ago
  And GLM is only 0.7T! But these labs distill off the larger models. Both officially at the labs with the big ones, and unofficially. We need the giant models to get the smaller models.
johnnyApplePRNG 2mo ago
Fable is most definitely nowhere near 10T. The cost to train and infer that would be insane, even by today's standards.
- nl 2mo ago
  Fable is strongly believed to be around 10T. The most conservative estimate I've seen is 8T. Eg: https://www.reuters.com/technology/bytedance-targets-mega-ai-model-nearing-anthropics-mythos-ft-reports-2026-08-07/ https://www.reuters.com/technology/bytedance-targets-mega-ai... That reports Mythos as 8T and Fable as 5T, but I think they mean Opus as 5T, which is widely known, eg: https://eu.36kr.com/en/p/3760679047267075?ref=explainx https://eu.36kr.com/en/p/3760679047267075?ref=explainx Both Grok and Bytedance are training 10T models.
  - andai 2mo ago
    Wasn't Opus ~1.5T and Fable is about twice that?
  - stymaar 2mo ago
    The fact that Musk claims Opus is 5T to justify why Grok is far behind should be taken with a massive grain of salt given he's a recidivist mythomaniac. Honestly if Opus is 5T parameters while being matched by the biggest open models that are at least twice smaller, it would mean that the US is already behind China in the AI race, despite a significant edge in compute.
    - WinstonSmith84 2mo ago
      Yes. And Opus goes a very long way compared to Fable, Anthropic isn't doing any favour, it's clearly just 2 models with a very different amount of parameters.
    - nl 2mo ago
      The open models don't really match Opus. For example I regularly do Fable+Opus agentic coding runs over 24 hours without intervention. I think I've had GLM do a run that was a few hours. That's the closest I've had an open model come on that kind of work.

alightsoul 2mo ago

pretty sure 10 trillion parameters is now the norm among closed ai labs, given that nvidia also references the same 10 trillion number for their nvl72 racks

KronisLV 2mo ago
Pretty bad efficiency then unless that only applies to Fable class but even then - Kimi K3 is around 3T and does similarly well in most benchmarks.
- esafak 2mo ago
  As others have noted, most benchmarks stress the torso, not the tail.

nl 2mo ago

Mythos/Fable are around 10T: > According to FT, industry estimates say Anthropic's most advanced Mythos 5 has about 8 trillion parameters and Fable 5 about 5 trillion https://www.reuters.com/technology/bytedance-targets-mega-ai-model-nearing-anthropics-mythos-ft-reports-2026-08-07/ https://www.reuters.com/technology/bytedance-targets-mega-ai... I believe this report has confused Opus (which is known to be around 5T) and Fable. Other reports say 10T. See for example https://eu.36kr.com/en/p/3760679047267075?ref=explainx https://eu.36kr.com/en/p/3760679047267075?ref=explainx where Musk talks about the models being trained on Colossus2

zozbot234 2mo ago
> I believe this report has confused Opus (which is known to be around 5T) and Fable. 5T for Opus feels quite high though. DeepSeek V4 Pro is a mere 1.6T and often described as a match with Opus in overall quality. Even the largest open models in common use are around 2.8T.
- Implicated 2mo ago
  > and often described as a match with Opus in overall quality It's not. Idk about who has more T's but, unfortunately, DS4 pro is not a match to Opus, at least not Opus 4.8.
- nl 2mo ago
  It's not an Opus match. The difference is very visible in long tail applications. Exactly where you'd expect parameter count to matter.
YmiYugy 2mo ago
I'm confused. I thought Mythos 5 and Fable 5 were exactly the same model just with a different security layer in front of it. Could they mean the Mythos 5 Preview?
- nl 2mo ago
  Yes. One reason why I think that report has confused Fable and Opus.

redox99 2mo ago

You don't really need to train a 10T model to test cerebras against a 10T model. You can feed it an untrained (randomly initialized) model and benchmark it. Result will be gibberish but performance the same.