Model Overview
Inferact/MiniMax-M3-EAGLE3-GQA is a grouped-query-attention (GQA) EAGLE3 draft model for accelerating inference of MiniMax-M3, served with vLLM and trained with TorchSpec.
It is retrained on the same datasets as the multi-head-attention version Inferact/MiniMax-M3-EAGLE3 — kimi-mtp, OpenCodeInstruct, SWE-bench, and SWE-bench-Pro — with the draft's attention changed from MHA to GQA (num_key_value_heads: 64 → 4) for inference efficiency (16× smaller draft KV cache) and compatibility with the target model.
The draft is a 1-layer dense Llama (LlamaForCausalLMEagle3) on MiniMax-M3's hidden_size=6144 / vocab_size=200064; at serve time it shares the target's embedding and LM head (EAGLE3). See config.json for the full architecture.
Performance
Mean accepted length and draft accept rate measured end-to-end against MiniMaxAI/MiniMax-M3-MXFP8 served with vLLM at tensor-parallel-size=4, num_speculative_tokens=3, greedy sampling (temperature=0, top_p=1.0), max-concurrency=16.
| Dataset | n | Mean accepted length | Draft accept rate | Per-position accept rate (pos 1 / 2 / 3) |
|---|---|---|---|---|
| MT-Bench | 64 | 2.668 | 55.62% | 0.745 / 0.537 / 0.387 |
| SPEED-Bench (qualitative) | 64 | 2.561 | 52.04% | 0.719 / 0.500 / 0.342 |