kimi-k2.7-code-eagle3-mla

kimi-k2.7-code-eagle3-mla

Model Overview

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.

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Quick Start

Requirements

• NVIDIA GPU with CUDA 12.0+
• vLLM >= 0.20.0

Launch Server (vLLM)

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.