Matryoshka: Speculative Decoding Speeds Up Inference by 10-30%
nthngdy · x · 2026-08-19
At inference time, speculative decoding is performed on the largest model using smaller submodels as drafts. Nesting allows shared memory and activations, amortizing draft overhead. This achieves 10-30% faster decoding. During training, all submodels are trained together, enabling free student-teacher online distillation during pretraining for better alignment.
Related event: Matryoshka LM Suites: Train an Entire Model Suite in One Nested Run(8 posts)→
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