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Speculative decoding can reduce the number of sequential target-model decoding steps by having a faster drafter propose tokens for the target model to verify. EAGLE-3 drafts autoregressively using learned target-model features; DFlash drafts a block with a diffusion model in one forward pass; xPress adds a causal refinement step to DFlash drafts. Which is faster in production depends on the drafter’s cost, verification work, workload, and implementation—not just the largest speedup reported in a paper.
What speculative decoding does
In ordinary autoregressive generation, a language model produces the next token, then uses it to produce the one after that. That creates a sequence of dependent target-model steps. Speculative decoding introduces a proposer, or drafter, that produces several candidate tokens. The target model then verifies candidates in parallel. If verification accepts a run of candidates, the target model can advance several output positions after one verification step rather than generating each token in a separate target-model iteration.
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The speed benefit is a systems trade-off. Drafting costs compute too, and the verifier must process the proposed candidates. A method helps only when the saved sequential target-model work outweighs those costs for the deployed model and workload. Acceptance length or acceptance rate is useful diagnostic data, but neither alone says how much faster a user’s request will finish.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute“Lossless” or distribution-preserving describes the verification procedure under its assumptions; it does not mean every run has the same wall-clock latency, or that every speculative implementation preserves the target model’s distribution regardless of configuration. The DFlash paper reports lossless acceleration for its tested settings, while the EAGLE-3 paper reports experimental speedups. For deployment, verify that the implementation’s sampling and verification behavior matches the intended target-model configuration.
#1 Best Overall
How EAGLE-3, DFlash, and xPress differ
These methods change how candidate tokens are drafted. Their published headline figures come from different experiments and comparisons, so they are not a direct three-way ranking.
| Method | Drafting approach | Reported result and scope | What to check in deployment |
|---|---|---|---|
| EAGLE-3 | A learned autoregressive drafter predicts tokens and fuses target-model features from multiple layers, using a training-time technique described by the authors. | The authors report up to 6.5× speedup in the paper’s experiments. This is the reported maximum, not a general production expectation. EAGLE-3 paper | Its draft path is sequential. Confirm that the official implementation and available checkpoint support the intended target model and serving configuration. EAGLE repository |
| DFlash | A lightweight block-diffusion drafter proposes a block in one forward pass, conditioned on context features extracted from the target model. | The authors report over 6× lossless acceleration across the models and tasks they tested, and up to 2.5× higher speedup than EAGLE-3 in their experiments. These are paper results, not a matched guarantee for other setups. DFlash paper | Check the exact block-drafting implementation and model configuration. The vLLM Speculators guide says sample_from_anchor should match the model configuration. DFlash guide |
| xPress | A lightweight causal refiner adds token dependencies to block-diffusion drafts before verification. | On Qwen3-8B across seven math, code, and chat benchmarks, the authors report about 30% average acceptance-length gain (up to 56%) and about 1.3× average end-to-end decoding throughput (up to 1.7×) over the original DFlash drafter. xPress paper | Interpret the result as a comparison against the named DFlash baseline on that model and benchmark suite. The project describes a benchmark harness and a vLLM V1 integration, not universal compatibility. xPress README |
Why DFlash drafts blocks differently
An autoregressive drafter predicts one token conditioned on the tokens before it, then predicts the next token from the expanded prefix. DFlash instead uses block diffusion to draft multiple positions in a single forward pass. Its drafter is conditioned on context features from the target model, which gives the proposal process information about the target’s state without requiring an autoregressive draft step for each position. The DFlash authors describe this as combining single-pass block drafting with context features to obtain high-quality drafts and higher acceptance rates. Read the DFlash paper.
Rank #2
That parallel proposal path changes the compute-versus-acceptance trade-off; it does not remove verification. The useful question is whether the time and memory cost of producing and checking a block are lower than the target-model work saved by accepting it.
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What xPress adds to DFlash
Block-diffusion drafting can miss dependencies between positions in a proposed block. xPress adds a lightweight causal refinement step to restore those dependencies and improve acceptance in the authors’ experiments. It is therefore best understood as a refinement to the DFlash approach, not as an independent basis for comparing all speculative-decoding methods.
Rank #3
The reported xPress gains have a specific baseline and scope: Qwen3-8B, seven math, code, and chat benchmarks, and comparison with the original DFlash drafter. The authors report an average acceptance-length increase of about 30%, with a maximum of 56%, and average end-to-end decoding throughput of about 1.3×, with a maximum of 1.7×. These numbers should not be transferred to a different target model, task mix, or serving stack without measuring it.
Does speculative decoding preserve output quality?
It can preserve the target model’s output distribution when the draft-and-verification algorithm is designed and configured to do so. The drafter proposes candidates; the target model remains responsible for verification. That is different from simply replacing the target model with a smaller model and accepting its outputs.
Rank #4
Distribution preservation is not the same as identical outputs across separate runs: sampling can produce different valid continuations. Nor does a “lossless” label guarantee that an arbitrary integration, setting, or mismatch between draft and target configurations has the required behavior. Keep the target checkpoint and decoding settings fixed in comparisons, and check quality and distribution behavior for the exact implementation you plan to serve.
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How to benchmark speculative decoding in vLLM
There is no apples-to-apples universal ranking in the reported headline values: the methods were evaluated in different experiments, on different model and task sets, and against different baselines. A deployment benchmark should answer whether a specific method improves your actual serving workload.
- Confirm compatibility first. Check the current implementation documentation for your target checkpoint, drafter checkpoint, framework release, and hardware. EAGLE’s official repository lists implementations and checkpoints; the vLLM Speculators DFlash guide covers its configuration; the xPress README describes its harness and vLLM V1 integration. Availability and support can change across releases. EAGLE repository, DFlash guide, xPress README
- Match the comparison conditions. Use the same target checkpoint, prompt set, output-length distribution, decoding and sampling settings, accelerator, precision, batch size, concurrency, context-length mix, serving framework/version, and warm-up procedure. If a method requires a drafter checkpoint or method-specific setting, record it rather than silently changing the baseline.
- Include representative request shapes. Test short answers as well as long structured generation, and use prompt/context lengths and concurrency that reflect expected traffic. A result from one task or batch regime may not predict another.
- Measure user-visible performance and mechanism. Record end-to-end latency and throughput, including time to first token where it matters. Also capture acceptance length or rate, drafter overhead, verifier cost, and memory use. Define throughput consistently—for example, generated tokens per second over the measured interval—and report the workload and concurrency alongside it.
- Check output behavior. Compare outputs or distribution-level behavior under the same decoding settings, using the verification guarantees and assumptions of the implementation. A speed result is not sufficient if the deployed configuration changes the required output behavior.
- Repeat and report the full setup. Use repeated runs after warm-up and report the model, prompt/output mix, hardware, software versions, settings, and latency/throughput statistics. This lets another engineer distinguish a drafter improvement from a change in workload or serving conditions.
For DFlash in vLLM Speculators, specifically check that sample_from_anchor matches the model configuration as directed by the guide. The vLLM project’s July 28, 2026 overview includes DFlash among its parallel-drafting algorithms; because integration status is version-sensitive, consult the documentation for the release you deploy. vLLM parallel drafting overview
Quick Recap
How to interpret the published speedups
- “Up to” is a maximum, not a typical-case promise. EAGLE-3’s up-to-6.5× figure and DFlash’s up-to-2.5× comparison against EAGLE-3 describe maxima in the authors’ experiments, not expected multipliers for every production request.
- “Average” is tied to a benchmark set. xPress’s averages apply to its Qwen3-8B comparison over seven specified benchmark categories; they do not establish an average across arbitrary workloads.
- Different baselines are not interchangeable. xPress compares against the original DFlash drafter, while DFlash also reports comparisons with EAGLE-3. Combining those figures into a single ranking would mix experimental conditions.
- Acceptance is not throughput. More accepted tokens can help, but drafter overhead, verification cost, batching, concurrency, and request lengths determine end-to-end results.
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