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Not in the ideal algorithm’s distributional sense—but it can still produce different text on a particular run. Speculative decoding uses a faster draft model to propose tokens and a target model to verify them. A rejection-sampling correction preserves the target model’s probability distribution under the algorithm’s assumptions; it does not make each sampled sequence identical across runs. Real implementations can also show numerical variation.
What speculative decoding guarantees
In ordinary autoregressive generation, the target model produces tokens in sequence. Speculative decoding speeds that process by having a draft model propose one or more tokens, then asking the target model to verify them. The target’s verification and a rejection-sampling correction allow the method to preserve the target model’s output distribution in the ideal algorithm.
In simplified terms, a proposed token can be accepted when it is consistent with the target’s distribution. If it is rejected, a correction draw accounts for probability mass the target assigns beyond the draft proposal. This lets the system use useful draft proposals without simply adopting the draft model’s distribution. The foundational 2022 paper describes the method as faster sampling without changing the target outputs’ distribution (Leviathan, Kalman, and Matias, “Fast Inference from Transformers via Speculative Decoding”); the 2023 paper describes modified rejection sampling for the same goal (Cai et al., “Accelerating Large Language Model Decoding with Speculative Sampling”).
Why the output can still differ
Same distribution does not mean same sample
A probability distribution describes how likely different outputs are, not which exact output must be drawn on a particular run. Even with the same target model and settings, stochastic sampling can produce different sequences. Speculative decoding’s ideal guarantee is that the method samples from the target distribution, not that it reproduces a specific sampled answer token for token.
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Finite precision and implementation details
The mathematical result assumes the algorithm’s probabilities are handled exactly. Actual computations use finite-precision numbers. The vLLM v0.21.0 documentation calls speculative sampling “theoretically lossless up to the precision limits of hardware numerics” and notes that floating-point differences can slightly change probabilities (vLLM: Speculative Decoding).
Batching and numerical behavior can matter too. vLLM says batch size may affect log probabilities and output probabilities because of non-deterministic batched operations or numerical instability. It also states that it does not currently guarantee stable token log probabilities. Small probability differences can affect which token is sampled, so two runs may diverge even when they use the same prompt and model.
How to interpret a different answer
- Different text alone does not show the distribution guarantee failed. Separate stochastic draws may differ while following the same probability law.
- A numerical change is a separate possibility. Finite precision, batch size, or other implementation behavior can slightly alter computed probabilities.
- Greedy decoding and stochastic sampling are not the same test. Greedy decoding selects the highest-probability token at each step; sampled generation draws from a distribution. vLLM documents greedy-sampling equality checks separately from rejection-sampler convergence checks.
When investigating variation, compare like with like: model and draft checkpoint, decoding settings, batch size, and runtime configuration. A reproducibility requirement should be tested in the exact serving setup rather than inferred from the ideal algorithm’s guarantee.
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Published speedups are results from specific experiments, not a universal multiplier. Leviathan, Kalman, and Matias reported a 2–3× acceleration on T5-XXL compared with the standard T5X implementation. Cai and colleagues reported a 2–2.5× decoding speedup in a distributed Chinchilla 70-billion-parameter benchmark. Those measurements belong to their stated model and setup; they do not predict the gain for every model or workload.
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A 2026 vLLM report on AMD GPUs found output-token throughput effects varied with drafting method and proposal length, as well as model family, draft checkpoint, workload, and acceptance behavior (Exploring Speculative Decoding in vLLM on AMD GPUs). In practice, judge a configuration by latency or output-token throughput on the workload you intend to serve, including its batch size and how often draft proposals are accepted. There is no universally best proposal length or drafting method established by these results.
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