AQ-MedAI/Ling-3.1-flash-DSpark

Thanks to the SpecForge framework for their foundational contributions.

Model Overview

Ling-3.1-Flash-DSpark is a self-trained DSpark draft model designed to accelerate Ling-3.1-Flash inference through speculative decoding.

Performance & Acceleration

We evaluate 5-token and 7-token drafting configurations across mathematical reasoning, code generation, dialogue, and knowledge benchmarks.

Both drafting configurations outperform the AR and MTP baselines across all evaluated benchmark–concurrency combinations.

Evaluation Configuration:

• Target model: Ling-3.1-Flash
• Decoding temperature: T = 0 (greedy decoding)
• Draft tokens: 5 or 7
• Concurrency: 1, 8, 16, and 32
• Benchmarks: GSM8K, MATH500, MBPP, HumanEval, MT-Bench, and C-Eval
• Metrics: Mean accepted length (τ) and throughput (tokens/s)

Results:

5 Draft Tokens

7 Draft Tokens

Quick Start

Requirements

• NVIDIA GPU
• CUDA 12.0+
• PyTorch 2.0+

Installation

pip install sglang==0.5.22

Inference with SGLang

python3 -m sglang.launch_server \
    --model-path /models/Ling-3.1-flash \
    --host 0.0.0.0 \
    --port 30010 \
    --mem-fraction-static 0.82 \
    --trust-remote-code \
    --tp 8 \
    --ep 8 \
    --speculative-algorithm DSPARK \
    --speculative-draft-model-path AQ-MedAI/Ling-3.1-flash-DSpark \
    --speculative-dspark-block-size 5

For the 7-token configuration, change:

--speculative-dspark-block-size 7

Citation

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

@misc{ling31-flash-dspark,
  title={Ling-3.1-Flash-DSpark: Accelerating Inference with DSpark},
  author={Ant AQ Team},
  year={2026},
}