SlimSpec Breaks Speculative Decoding Bottleneck: 5x Speedup Without Cutting Vocabulary
Arindam_1729 · x · 2026-07-22
Nebius introduced SlimSpec, solving a critical bottleneck in LLM speculative decoding. In traditional methods, even with a tiny draft model, the LM head still projects hidden states into a massive vocabulary (e.g., 128K tokens), causing massive computational overhead.
Existing vocabulary trimming approaches (like FR-Spec, VocabTrim) reduce compute but cap the acceptance rate because the drafter can never propose critical tokens that have been removed.
SlimSpec innovatively compresses the representation rather than the vocabulary. By keeping all tokens intact, it reduces LM-head cost by 4-5x, delivering up to 8-9% higher end-to-end speedup versus baselines.
Related event: Nebius Unveils SlimSpec for Faster Speculative Decoding(2 posts)→
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