Algorithm Decision Guide

Speculators currently supports four speculative decoding algorithms: Eagle-3, P-EAGLE, DFlash, and MTP. All are lossless -- they produce output from the same distribution as the target model.

How They Differ

Eagle-3 predicts draft tokens autoregressively, one at a time.

P-EAGLE extends Eagle-3 with parallel multi-token prediction across multiple depths, using COD sampling for memory-efficient training.

DFlash predicts all draft tokens in a single forward pass using block-based prediction with anchor points.

MTP finetunes the model's native multi-token prediction head on domain-specific data. Unlike the other algorithms, MTP does not train from scratch -- it starts from pre-existing MTP layers and is only available for models with native MTP support.

Eagle-3, P-EAGLE, and DFlash can be paired with any supported verifier model (including quantized variants) -- the draft architecture is independent of the verifier architecture. MTP requires a model with native MTP layers (e.g. Qwen3-Next, Qwen3.5).

Current Support

Eagle-3 has been available longer and has broader support in both Speculators and vLLM. P-EAGLE, DFlash, and MTP were added more recently and support is improving rapidly.

Which Should I Use?

If you're unsure, start with Eagle-3 -- it has the most mature tooling and documentation. If you want parallel multi-token prediction with an Eagle-3-based architecture, try P-EAGLE. If you want to experiment with DFlash's single-forward-pass block prediction approach, the training workflow is the same. If your model already has native MTP layers (e.g. Qwen3-Next, Qwen3.5), MTP finetuning lets you improve the existing MTP head on domain-specific data without training a separate draft model.

For more details on each algorithm, see:

• Eagle-3
• P-EAGLE
• DFlash
• MTP