Applied Compute(@appliedcompute):At Kimi K3 scale, memory directly determines how many GPUs you need for training. For example, streaming gradients for Adam updates cuts the host memory peak by ~33%. Another bottleneck was SiTU-GLU activations. Naive autograd saves redundant tensors and consumes >100GB of HBM at ~100k tokens/GPU. A custom operator that streams through a fixed-size workspace saves ~90GB of HBM per GPU.

Applied Compute

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Applied Compute@appliedcompute

2026.08.28 19:48

At Kimi K3 scale, memory directly determines how many GPUs you need for training. For example, streaming gradients for Adam updates cuts the host memory peak by ~33%. Another bottleneck was SiTU-GLU activations. Naive autograd saves redundant tensors and consumes >100GB of HBM at ~100k tokens/GPU. A custom operator that streams through a fixed-size workspace saves ~90GB of HBM per GPU.

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