"You're absolutely right!"

H100 is not an everyday product. Laptop is

On that note you can rent an H100 for an hour for under $10 which might make for a slightly more interesting test, whats the best model outcome you can train in under an hour.

We can / should benchmark and optimize this to death on all axes

What exactly is your point? That instead of expressing workloads in terms of what a laptop could do, you prefer to express them in terms of what a MacBook Pro could do?

Of course that only works if the trial runs are representative of what your full scale model will look like. But within those constraints optimising training time seems very valuable

  32 CPU
  80 GPU
  512GB RAM

Might still be early days I’m trying to use the model to sort my local notes but I don’t know man seems only a little faster yet still unusable and I downloaded the lighter qwen model as recommended.

Again it’s early days maybe I’m being an idiot I did manage to get it to parse one note after about 15 mins though.

As an alternative you might consider a Ryzen Pro 395+ like in the Framework desktop or HP Zbook G1a but the 128GB versions are still extremely expensive. The Asus Flow Z13 is a tablet with ryzen 395+ but hardly available with 128GB

Neural-type models have long passed the point where markov chains made any sense by many orders of magnitude.

Markov models fail by being too opinionated about the style of compute.

In contrast, a linear tensor + non-linear function has incredible flexibility to transform the topology of information. Given large enough tensors, two such layers, with recurrence, can learn any mapping, static or dynamical. No priors (other than massive compute) needed.

All other neural architectures then are simply sparser arrangements, that bring compute demands down. Where the sparseness is fit to the type of problem.

Sparseness can be deeper but narrower information flows (thus “deep” learning). Or in lower numbers of weights to weight application (I.e. shared weights, like convolutions).

That said, it does make it easier to claim progress...

Makes me want to try training a model to sing "Daisy, Daisy..."

The podcast itself with one of the authors was fantastic for explaining and discussing the capabilities of LLMs more broadly, using this small controlled research example.

As an aside: i dont know what the dataset is in the biological analogy, maybe the agar plate. A super simple and controlled environment in which to study simple organisms.

For ref: - Podcast ep https://www.cognitiverevolution.ai/the-tiny-model-revolution... - tinystories paper https://arxiv.org/abs/2305.07759

That boat will float your goat!

I think you meant Llama.

The rhymes are admittedly more limited, unless you have a Boston accent.

The lesson here is that you can't use a laptop to train a useful model - at least not without running that training for probably decades.

That doesn't mean you can't run a useful model on a laptop that was trained in larger hardware. I do that all the time - local models hit really good this year.

> reducing model size while retaining capability will just never happen.

Tell that to Qwen3-4B! Those models are remarkably capable.

There's also a whole world of data curation to improve training, which is likely to be great for small models and seems still underexplored.

[0] https://www.digitaltrends.com/computing/laptop-gpu-power-lim... [1] https://coconote.app/notes/4c75b7a0-eb41-435d-85ee-55ae2dd8d...