[leogao]

For what it's worth, we think it's unfortunately quite unlikely that frontier models will ever be trained with extreme unstructured sparsity, even with custom sparsity optimized hardware. Our main hope is that understanding sub-frontier models can still help a lot with ensuring safety of frontier models; an interpretable GPT-3 would be a very valuable object to have. It may also be possible to adapt our method to only explaining very small but important subsets of the model.

[m_ke]

yeah it's not happening anytime soon, especially with the whole economy betting trillions of dollars on brute fore scaling of transformers on manhattan sized GPU farms that will use more energy than most mid western states.

Brains do it somehow, so sparsely / locally activated architectures are probably the way to go long term, but we're decades away from that being commercially viable.

[esafak]

As the lead author, why do you think so?

[leogao]

I'm not an expert at hardware, so take this with a grain of salt, but there are two main reasons:

- Discrete optimisation is always going to be harder than continuous optimization. Learning the right sparsity mask is fundamentally a very discrete operation. So even just matching fully continuous dense models in optimization efficiency is likely to be difficult. Though perhaps we can get some hope from the fact that MoE is also similarly fundamentally discrete, and it works in practice (we can think of MoE as incurring some penalty from imperfect gating, which is more than offset by the systems benefits of not having to run all the experts on every forward pass). Also, the optimization problem gets harder when the backwards pass needs to be entirely sparsified computation (see appendix B).

- Dense matmuls are just fundamentally nicer to implement in hardware. Systolic arrays have nice predictable data flows that are very local. Sparse matmuls with the same number of flops nominally only need (up to a multiplicative factor) the same memory bandwidth as an equivalent dense matmul, but they need to be able to route data from any memory unit to any vector compute unit - the locality of dense matmuls means that the computation of each tile only requires a small slice of both input matrices, so we only need to load those slices into shared memory; on the other hand, because GPU-to-GPU transfers are way slower, when we op-shard matmuls, we replicate the data that is needed. Sparse matmuls would need either more replication within each compute die, or more all-to-all internal bandwidth. This means spending way more die space on huge crossbars and routing. This would cost a lot of die space, though thankfully, the crossbars consume much less power than actual compute, so perhaps this could match dense in energy efficiency and not make thermals worse.

It also seems very likely that once we create the interpretable GPT-1 (or 2, or 3) we will find that making everything unstructured sparse was overkill, and there are much more efficient pretraining constraints we can apply to models to 80/20 the interpretability. In general, a lot of my hope routes through learning things like this from the intermediate artifact (interpretable GPT-n).

To be clear, it doesn't seem literally impossible that with great effort, we could create custom hardware, and vastly improve the optimization algorithms, etc, such that weight-sparse models could be vaguely close in performance to weight-dense models. It's plausible that with better optimization the win from arbitrary connectivity patterns might offset the hardware difficulties, and I could be overlooking something that would make the cost less than I expect. But this would require immense effort and investment to merely match current models, so it seems quite unrealistic compared to learning something from interpretable GPT-3 that helps us understand GPT-5.

[m_ke]

Yes it would require completely new hardware and most likely ditching gradient descent for alternative optimization methods, though I'm not convinced that we'd need to turn to discrete optimization.

Some recent works that people might find interesting:

- Evolution Strategies at the Hyperscale - https://eshyperscale.github.io/

- Introducing Nested Learning: A new ML paradigm for continual learning - https://research.google/blog/introducing-nested-learning-a-n...

- Less is More: Recursive Reasoning with Tiny Networks - https://arxiv.org/abs/2510.04871

- Nemotron Elastic: Towards Efficient Many-in-One Reasoning LLMs - https://arxiv.org/abs/2511.16664

[yvdriess]

A note on the hardware part: it does not require NN-specific hardware akin to neuromorphic. Sparse compute oriented architectures already have been developer for other reasons, such as large scale graph analysis or inference. It will still require significant effort to use it to train large models, but it would not be starting from scratch.