kimi-k2.7-code-eagle3-mla is an Eagle3 MTP draft model with MLA (Multi-Latent Attention) for accelerating inference of Kimi-K2.7-Code under vLLM speculative decoding. The draft proposes num_speculative_tokens candidate tokens per step; the Kimi-K2.7-Code verifier accepts them in parallel, so the output distribution is identical to plain autoregressive decoding while decode throughput improves.

Why an MLA (Multi-Latent Attention) Draft Model

Compared with an MHA draft model, the MLA variant is a better fit for Kimi-K2.7-Code deployment:

Uses less KV cache, which reduces serving memory pressure.
Matches Kimi-K2.7-Code's MLA architecture, so it fits more naturally into the inference engine's KV-cache handling under different serving scenarios such as PD-Disaggregation.

Architecture

Algorithm: EAGLE-3 with MLA, single draft decoder layer.
Verifier: Kimi-K2.7-Code. The draft reuses the verifier's frozen embedding / lm_head / norm and trains one MLA decoder layer plus an auxiliary-hidden-state fusion layer.
Shared input embedding: the input embedding is not shipped in this checkpoint — it is loaded from the verifier at inference (vLLM's Eagle3DeepseekV2ForCausalLM shares the target's embed_tokens when the draft weights omit it). This keeps the checkpoint compact (~3.7 GB).
Draft vocabulary: full 163,840-token vocabulary (no truncation).

Training Setup

Framework: Camelot, an online speculative-decoding training framework — FSDP training and vLLM inference run concurrently, with the verifier continuously generating fresh training data.
Training data: Kimi-K2.7-Code native data (agentic / coding / tool trajectories and re-answered prompts).
Schedule: cosine LR 2e-5, sequence length 8192, ttt_steps=4.

Performance

The primary metric is accept_length — the average number of tokens accepted per speculation step with num_speculative_tokens=3. Higher is better.

Benchmarks were run on vLLM 0.20.0 (TP=8, greedy decoding, concurrency=1) against the Kimi-K2.7-Code verifier.

Category	Benchmark	N	Accept Length
Dialogue	MTBench	80	2.427
Chinese	CEval	212	2.348
Math	GSM8K	500	3.201
Code	HumanEval	164	2.738
Math	MATH500	500	2.918
Math	AIME	30	2.542
Code	LiveCodeBench	200	2.362
Code	SPEED-Bench (coding)	80	2.515

Quick Start

Requirements

NVIDIA GPU with CUDA 12.0+
vLLM >= 0.20.0

Launch Server (vLLM)

bash

vllm serve moonshotai/Kimi-K2.7-Code \
    --tensor-parallel-size 8 \
    --speculative-config '{"model": "novita/kimi-k2.7-code-eagle3-mla", "method": "eagle3", "num_speculative_tokens": 3}' \
    --trust-remote-code

Launch Server (SGLang)

MLA Eagle3 draft model is not yet supported in SGLang. Will update once support is available.