Speculative Decoding¶

This document shows how to use Speculative Decoding with vLLM to reduce inter-token latency under medium-to-low QPS (query per second), memory-bound workloads.

To train your own draft models for optimized speculative decoding, see vllm-project/speculators for seamless training and integration with vLLM.

vLLM Speculation Methods¶

vLLM supports a variety of methods of speculative decoding. Model-based methods such as EAGLE, MTP, draft models, PARD and MLP provide the best latency reduction, while simpler methods such as n-gram and suffix decoding provide modest speedups without increasing workload during peak traffic.

• EAGLE
• Multi-Token Prediction (MTP)
• Draft Model
• Parallel Draft Model (PARD)
• Multi-Layer Perceptron
• N-Gram
• Suffix Decoding

Method Selection at a Glance¶

Use this qualitative table as a starting point for method selection. Real gains depend on your model family, traffic pattern, hardware, and sampling settings.

For reproducible measurements in your environment, use examples/offline_inference/spec_decode.py or the benchmark CLI guide.

--speculative-config schema¶

Use --speculative-config to pass speculative decoding settings as a JSON object on the CLI:

vllm serve <target-model> \
  --speculative-config '{
    "method": "draft_model",
    "model": "<draft-model>",
    "num_speculative_tokens": 5
  }'

The same keys are accepted from Python via LLM(..., speculative_config={...}). The tables below highlight common user-facing keys accepted in this JSON object; they are not an exhaustive schema reference. For more details, see the generated engine arguments reference and the API docs for vllm.config.SpeculativeConfig.

Common keys¶

These keys are commonly used across speculative decoding setups, though some only apply to model-based methods such as draft_model, mtp, eagle3, and dflash.

Method-specific keys¶

N-gram¶

Example:

vllm serve <target-model> \
  --speculative-config '{
    "method": "ngram",
    "num_speculative_tokens": 4,
    "prompt_lookup_min": 2,
    "prompt_lookup_max": 5
  }'

Suffix decoding¶

Example:

vllm serve <target-model> \
  --speculative-config '{
    "method": "suffix",
    "num_speculative_tokens": 8,
    "suffix_decoding_max_tree_depth": 24,
    "suffix_decoding_max_cached_requests": 10000,
    "suffix_decoding_max_spec_factor": 1.0,
    "suffix_decoding_min_token_prob": 0.1
  }'

Notes¶

• --speculative-config expects a JSON object on the CLI. In YAML config files, use a nested mapping instead of an escaped JSON string.
• tensor_parallel_size is not a valid key in speculative_config. Use draft_tensor_parallel_size instead.
• Keys such as temperature and top_p are sampling parameters, not --speculative-config fields.
• Internal fields such as target_model_config, draft_model_config, target_parallel_config, draft_parallel_config, and draft_load_config are populated by vLLM and are not intended to be set by users.

Lossless guarantees of Speculative Decoding¶

In vLLM, speculative decoding aims to enhance inference efficiency while maintaining accuracy. This section addresses the lossless guarantees of speculative decoding, breaking down the guarantees into three key areas:

1. Theoretical Losslessness - Speculative decoding sampling is theoretically lossless up to the precision limits of hardware numerics. Floating-point errors might cause slight variations in output distributions, as discussed in Accelerating Large Language Model Decoding with Speculative Sampling

2. Algorithmic Losslessness - vLLM’s implementation of speculative decoding is algorithmically validated to be lossless. Key validation tests include:

   • Rejection Sampler Convergence: Ensures that samples from vLLM’s rejection sampler align with the target distribution. View Test Code
   • Greedy Sampling Equality: Confirms that greedy sampling with speculative decoding matches greedy sampling without it. This verifies that vLLM's speculative decoding framework, when integrated with the vLLM forward pass and the vLLM rejection sampler, provides a lossless guarantee. Almost all of the tests in tests/spec_decode/e2e. verify this property using this assertion implementation

3. vLLM Logprob Stability - vLLM does not currently guarantee stable token log probabilities (logprobs). This can result in different outputs for the same request across runs. For more details, see the FAQ section titled Can the output of a prompt vary across runs in vLLM? in the FAQs.

While vLLM strives to ensure losslessness in speculative decoding, variations in generated outputs with and without speculative decoding can occur due to following factors:

• Floating-Point Precision: Differences in hardware numerical precision may lead to slight discrepancies in the output distribution.
• Batch Size and Numerical Stability: Changes in batch size may cause variations in logprobs and output probabilities, potentially due to non-deterministic behavior in batched operations or numerical instability.

For mitigation strategies, please refer to the FAQ entry Can the output of a prompt vary across runs in vLLM? in the FAQs.

Known Feature Incompatibility¶

1. Pipeline parallelism is not composible with speculative decoding as of vllm<=0.15.0
2. Speculative decoding with a draft models is not supported in vllm<=0.10.0

Resources for vLLM contributors¶

• [vLLM Office Hours #40] Intro to Speculators
• A Hacker's Guide to Speculative Decoding in vLLM
• What is Lookahead Scheduling in vLLM?
• Information on batch expansion
• Dynamic speculative decoding