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Ring All-Reduce: GPU Cluster Gradient Sync

Interactive 3D ring all-reduce simulator: watch gradient chunks pass around a ring of GPUs during distributed frontier-model training, tune cluster size, model parameters and interconnect bandwidth, and see when communication — not compute — becomes the bottleneck.

Machine Learning & Neural Networks3DAdvanced60 FPS📱 Mobile-adapted⇄ 2D version

Frontier-model scaling laws describe how loss falls with more compute — but reaching that compute in practice means synchronizing gradients across hundreds or thousands of GPUs every single training step. This simulator renders a 3D ring of GPU nodes running the ring all-reduce algorithm used by NCCL in every large-scale PyTorch/JAX training run: gradient chunks are scattered and reduced around the ring, then gathered back so every GPU ends the step with the identical averaged gradient. Tune the cluster size, model parameter count, interconnect bandwidth and per-GPU compute throughput, then run a training step to watch the compute-vs-communication split and see exactly when a cluster tips from compute-bound to communication-bound — the real infrastructure constraint behind compute-optimal training.

⚙ Under the hood

distributed traininggpu clusterall-reducescaling lawsfrontier modelsinterconnect

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install