Making Knowledge Distillation Cheap Enough to Run at Scale

Deploying state-of-the-art language models often requires multiple terabytes of VRAM, and the distillation step traditionally mirrors that demand because both teacher and student must be resident in memory. For a 120-billion-parameter model with a 200k-token vocabulary, a single training iteration can exceed 250 GB of VRAM, which is beyond the capacity of even the most powerful GPUs currently available. This memory pressure has forced teams to rely on large GPU farms and complex tensor-parallelism schemes.

The first change replaces the online teacher pass with an offline cache of the teacher’s top-K logits per token position. By pre-computing and storing only the most likely tokens, the teacher model never needs to be loaded during the student’s training loop, and the same cache can be reused for multiple experiments. This eliminates the teacher’s weight and activation memory from the training footprint entirely.

The second change redesigns the KL-divergence loss to operate in a fused, chunked manner. Instead of materialising a dense matrix that spans the entire vocabulary for every sequence token, the loss processes the data in smaller blocks, never allocating the full vocab × seq-length grid. Empirical results show the peak VRAM drops from roughly 250 GB to about 128 GB, fitting comfortably within a single H200-class GPU.

Together these system tweaks make it possible to run long-context distillation on a single GPU, dramatically reducing the cost of large-scale model compression and opening the door to more rapid experimentation. However, the approach assumes the teacher’s output distribution does not change during training and that the top-K approximation captures enough information; rare token probabilities are discarded, which could affect the fidelity of the student model in edge cases.

Adopting the method requires adding a preprocessing step to generate and store the top-K logits cache, as well as integrating the custom fused chunked KL loss into the training codebase. No modifications to model architectures are needed, but engineers must ensure sufficient fast storage for the cached logits and verify that the new loss implementation is correctly validated against the dense baseline. The technique remains bounded by the GPU’s memory ceiling, so extremely large vocabularies or sequence lengths that still exceed ~128 GB will need additional engineering or larger hardware.