Batch Speculative Decoding Done Right
Authors: name: Ranran Haoran Zhang, name: Soumik Dey, name: Ashirbad Mishra, name: Hansi Wu, name: Binbin Li, name: Rui Zhang
Organization: Pennsylvania State University

Speculative decoding speeds up LLM inference by using a small draft model to
propose multiple tokens that a target model verifies in parallel. Extending
this idea to batches is essential for production serving, but it introduces the
ragged tensor problem: sequences in the same batch accept different numbers of
draft tokens, breaking right-alignment and corrupting position IDs, attention
masks, and KV-cache state. We show that several existing batch implementations
violate output equivalence-the fundamental requirement that speculative
decoding must produce identical token sequences to standard autoregressive
generation. These violations occur precisely due to improper handling of the
ragged tensor problem. In response, we (1) characterize the synchronization
requirements that guarantee correctness, (2) present a correctness-first batch
speculative decoding EQSPEC that exposes realignment as consuming 40% of
overhead, and (3) introduce EXSPEC, which maintains a sliding pool of sequences
and dynamically forms same-length groups, to reduce the realignment overhead
while preserving per-sequence speculative speedups. On the SpecBench dataset,
across Vicuna-7B/68M, Qwen3-8B/0.6B, and GLM-4-9B/0.6B target/draft pairs, our
approach achieves up to 3times throughput improvement at batch size 8
compared to batch size 1, with efficient scaling through batch size 8, while
maintaining 95% output equivalence. Our method requires no custom kernels and
integrates cleanly with existing inference stacks. Our code is available at
https://github.com/eBay/spec_dec.