We’ve added a new History page, detailing a summary of the ZSpaceLabs.ai development history.

The Big Project: The Hot Dense Grid

The over-arching goal of the ZSpaceLabs work is something I call the “Hot Dense Grid”.

“The Hot Dense Grid” is shorthand for:

  • coherent dense finite-grid space-time dynamics,
  • hosted on GPU-accelerated tensors,
  • distributed across a cluster of GPUs,
  • such that the compute and network throughput is maximized,
  • and iterative development cost is minimized.

This problem is interesting because many important physics and engineering problems can be represented on a finite-grid space-time dynamics model; so common foundations can be shared across many different problems.

Interestingly, there’s a very strong overlap between the Hot Dense Grid and distributed AI problems. Both need high-performance tensor processing, cluster GPU acceleration, and effective development tools.

The pathway I’ve been pursuing is to identify the tensor expression environment which is closest aligned to high-performance distributed tensor simulation; and then work backwards to identify gateway problems (maximum payoff to the community for minimum investment) in the current rust tensor/AI stacks which would garner additional R&D towards the long term problem.

Despite its popularity for AI development at present and for benchtop notebook evaluation, Python is not a good fit for high-performance computing. It lacks meaningful compiler tooling, strong asynchronous programming, and the kind of strong typing and tooling support needed for rapid development thread-safe highly parallel environments.

I’ve explored a number of different approaches to solving this problem, and I’ve come to the conclusion that the rust burn library has the best chance of growing into a viable high-performance tensor expression environment.

The Road So Far

The work spans roughly 15 months, from early 2025 through May 2026, across six repositories. The through-line is the Hot Dense Grid thesis: build burn into a capable distributed tensor computing platform, validate the API with real model training and physics simulation, and establish tooling that makes the ecosystem usable by others.

There are three main threads that run concurrently and reinforce each other:

  1. Upstream burn contributions — adding missing tensor primitives, fixing API ergonomics, and extending the distributed/training infrastructure.
  2. bimm + bimm-contracts — an image model training framework that exercises the burn API, acts as a test bench for shape-contract tooling, and pushes toward SOTA image classification.
  3. wordchipper / zsl-chat — building a production-quality Rust BPE tokenizer (compatible with tiktoken/OpenAI vocabularies) and LLM training infrastructure, which drives further burn optimizer and record API work.
  4. clockmill — physics simulation demos (Conway’s Game of Life, D2Q9 Lattice-Boltzmann) that demonstrate the Tensor::unfold() machinery and prove the folded-window simulation approach works.