Offline KD Boosts Throughput 41% on Single H200, Slashes LLM Distillation Memory
MultiverseComputingCAI · hf · 2026-08-10
To address the high cost of knowledge distillation (KD) for deploying large language models under tight constraints, Multiverse Computing introduced an efficient distillation approach featuring two major system-level optimizations:
- Offline Top-K Logits Caching: By caching the teacher's top-K logits in advance and training the student against this cache, the method removes the teacher from memory. This achieves near-identical training loss compared to online distillation, while running about 29% faster per iteration and delivering up to 41% higher throughput on a single H200 GPU.
- Fused Chunked KL Loss: This technique avoids materializing the full vocabulary-sized logit tensor, making peak memory linear with the sequence length. It eliminates the memory spikes that typically cap context length, enabling training at 4x the context (32,768 tokens) on a single GPU.
The team also reported ablations on loss design and sequence packing, and has open-sourced their chunked-loss implementation.
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