AQ-MedAI/GLM-5.1-eagle3

Thanks to the SpecForge framework for their foundational contributions. Stay tuned for further updates.

Model Overview

GLM-5.1-eagle3 is an advanced and highly specialized draft model meticulously engineered to significantly accelerate the inference process of the GLM-5.1 ecosystem, leveraging the powerful EAGLE3 framework.

Architected upon the robust Llama architecture, this model functions as an exceptionally efficient drafter. It has undergone rigorous training on 1 million high-quality samples sourced from the comprehensive open-perfectblend dataset and some multimodal data. This extensive training ensures precise and strict alignment with the teacher model's distribution, thereby guaranteeing high fidelity and performance.

Performance & Acceleration

The core value of this EAGLE3 model is its ability to predict multiple future tokens that are subsequently verified by the base model. High acceptance lengths indicate significant latency reduction. Continuous future iterations.

Speculative Decoding Configuration:

• --speculative-num-steps 3: Configures the number of speculative decoding steps.
• --speculative-eagle-topk 1: Sets the top-k value for the Eagle draft model during speculative decoding.
• --speculative-num-draft-tokens 4: Specifies the number of draft tokens generated in each speculative step.

MTP vs Eagle3 Performance Comparison: Batch Sizes (bs) 1 and 32

Despite Eagle3's slightly lower accept length compared to MTP, it achieves higher output throughput across most benchmarks, indicating superior overall efficiency.

Throughput Comparison (token/s)

Stage	MTP (bs=32)	Eagle3 (bs=32)	MTP (bs=1)	Eagle3 (bs=1)	bs=32 Advantage (MTP/Eagle3)	bs=1 Advantage (MTP/Eagle3)
mtbench	1127.30	1129.48	151.25	146.83	-0.2%	+3.0%
humaneval	1292.04	1369.01	167.58	175.04	-5.6%	-4.3%
gsm8k	682.24	686.80	134.78	133.23	-0.7%	+1.2%
math500	1648.05	1703.82	180.72	183.13	-3.3%	-1.3%

Accept Length Comparison

Stage	MTP (bs=32)	Eagle3 (bs=32)	MTP (bs=1)	Eagle3 (bs=1)	bs=32 Difference (MTP-Eagle3)	bs=1 Difference (MTP-Eagle3)
mtbench	2.93	2.78	2.86	2.70	+6.9%	+6.0%
humaneval	3.23	3.24	3.21	3.27	-0.3%	-1.8%
gsm8k	3.14	3.00	3.14	3.00	+4.7%	+4.4%
math500	3.40	3.35	3.41	3.37	+1.5%	+1.3%

Quick Start

Requirements

• NVIDIA GPU
• CUDA 12.0+
• PyTorch 2.0+

Installation

pip install sglang==0.5.10

Inference with SGLang

Eagle3

python3 -m sglang.launch_server \
  --model-path zai-org/GLM-5.1-FP8 \
  --tp-size 8 \
  --speculative-algorithm EAGLE3 \
  --speculative-draft-model-path AQ-MedAI/GLM-5.1-Eagle3 \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --mem-fraction-static 0.8 \
  --host 0.0.0.0 --port 30019 --attention-backend fa3

MTP

python3 -m sglang.launch_server \
  --model-path zai-org/GLM-5.1-FP8 \
  --tp-size 8 \
  --speculative-algorithm EAGLE \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --mem-fraction-static 0.8 \
  --host 0.0.0.0 --port 30019 --attention-backend fa3

Citation

If you use this model in your research or application, please cite the following:

@misc{glm5.1eagle3,
  title={GLM-5.1-eagle3: Accelerating Instruction Following with EAGLE3},
  author={Ant AQ Team},
  year={2026},
}