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Comments (52)

• woadwarrior01
  They have a history of making grandiose claims like this[1] from 2024, with no visible products or research.[1]: https://magic.dev/blog/100m-token-context-windows (also linked to in their blogpost)
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  simonw
  > We match DeepSeek V4 Pro Base using ~50x fewer FLOPs – that’s around half of GPT3’s pretraining compute, or ~$0.5M on GB200.If this holds up that's a really big deal.
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  pvillano
  A lot of people are betting their money on infinite growth forever of AI performance, compute usage, user base, subscription price.I think cost will decrease forever.
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  ansk
  I don't know enough about the specific models they're comparing against to say this definitively, but it looks to me like they're comparing their pre-trained models with others' post-trained models.The metric upon which their 10x claim is based (bits-per-byte) is exactly the metric which is optimized during pre-training. Post-trained models are fine-tuned to optimize other metrics, which is known to be detrimental to performance on bits-per-byte evaluations. So bits-per-byte evaluations will always make a pre-trained model look favorable in comparison to a comparable model which has also undergone post-training.Can someone confirm whether the models they are comparing against (DeepSeek V4, Kimi K2, and Nemotron 3 Ultra) have been post-trained?
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  ismael_rr
  Super awesome. Wish they would release the paper about what they did to achieve this. I remember nous released the token superposition paper which improved pretraining FLOPs some, but not 50x: https://nousresearch.com/token-superposition. Wondering if they also found some cool tokenization strategiesa
• brrrrrm
  this is basically the only thing pre-training teams work on in labs. compute efficiency is the metric, the assumption that scaling = intelligence is considered a given.
• riazrizvi
  Very helpful thanks.
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  monneyboi
  Imagine the sheer amount of power you could save by releasing the paper.
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  vatsachak
  Cool story. If it's true the company will be bought by open AI/Anthropic and Chinese labs will discover the trick and open source it by next quarter.
• vkaku
  This is great. All algorithmic efficiencies are amazing!One thing I'd remind all scientists and the wonderful people here is this wonderful meme/line from Jurassic Park: "Your scientists were so preoccupied with whether they could they didn't stop to think if they should."What is the actual amount of data that needs to be pre-trained and what is not? Nobody has come up with great answers to this question, and I'm already seeing amazing 0.5b-2b parameter models working very well with n-Gram corpuses of data. So, how many parameters do you really need for a given workload?
• mohsen1
  [dead]
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  pvillano
  Imagine yourself the CEO of a big AI company. It takes about a month to develop and train a model, so you release a new model every month. A startup says they can 10x your efficiency. What does that get you? You can't release a new model every three days. You can't 10x R&D either. You definitely can't tell investors that you are growing at the same rate, but selling off assets and cancelling purchasing contracts. So you just never improve efficiency enough to use less energy than the previous model version.I don't believe this is actually happening.
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  FailMore
  If you're like me, a SWE who is curious about ML/LLM training but unfamiliar with the terms, I got an agent to explain to me how to read the charts.Basically, you can think of a LLM as a function which generates a probability distribution of words. If the next word in a series is "they", and one model predicts that word 40% of the time, and another model predicts that word 1% of the time, the latter model is worse as it is more surprised by the true distribution.You can convert these probabilities into "bits":surprise in bits = −log₂(probability of the actual token) Probability of actual token,Surprise 1,0 bits 1/2,1 bit 1/8,3 bits 1/1024,10 bits This is then normalised by text length:Bits per byte = total next-token surprise in bits / number of bytes in the evaluated textSo the lower you go on the charts, the less surprises in the LLMs distribution (a better model).For more info: https://smalldocs.org/s/DfvdGuFsiR3LlzXw1H5J0K#k=AJ8V1AQECYj...