Qwen3.8 27B DSpine B16

Qwen3.8 27B DSpine B16 by tencent, a text-generation model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

Comparison

Feature	Qwen3.8 27B DSpine B16	Interfaze
Input Modalities	text, image, video, document	image, text, audio, video, document
Native OCR	No	Yes
Long Document Processing	No	Yes
Language Support	80 partial	162+
Native Speech-to-Text	No	Yes
Native Object Detection	No	Yes
Guardrail Controls	No	Yes
Context Input Size	1M	1M
Tool Calling	Yes	Tool calling supported + built in browser, code execution and web search

Scaling

Feature	Qwen3.8 27B DSpine B16	Interfaze
Scaling	Self-hosted/Provider-hosted with quantization	Unlimited

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View model card on Hugging Face

DSpine generates draft tokens in parallel. Each layer passes predicted predecessor features to adjacent positions, carrying causal dependencies through network depth.

This checkpoint is a five-layer draft model for Qwen3.8-27B, with block size 16 and an 8,192-token sliding window.

Evaluation

Runtime: SGLang 0.5.22, BF16, FlashAttention 3, concurrency 8.
Draft blocks: DSpine 16; official DFlash2 8.
DSpine inference: dynamic verification referencing DSpark, with ReplaySSM enabled.
General benchmarks: xhigh reasoning, greedy and temperature sampling, at most 4,096 new tokens.
Agent benchmarks: We evaluate WorkBuddy Bench (Code/Web/Office) using CodeBuddy sandbox trajectories: low/xhigh reasoning, temperature sampling, 20 conversations per subset, all tool-call prefixes replayed in order, prefix caching, and up to 32,768 new tokens per request.
Temperature sampling: temperature=1.0, top_p=0.95, top_k=20.

Results

General Benchmark Acceptance Length

Task	DFlash2(T=0)	DSpine(T=0)	DFlash2(T=1)	DSpine(T=1)
GSM8K	5.69	7.20	5.45	6.31
MATH-500	5.49	6.49	5.27	5.66
HumanEval	4.87	5.71	4.34	4.71
MBPP	5.04	6.14	4.79	5.41
MT-Bench	4.11	4.48	3.89	4.03

Agent Acceptance Length

Agent End-to-End Throughput

Throughput is total output tokens divided by end-to-end concurrent replay wall time (tokens/s). Parentheses show speedup over autoregressive decoding (AR).

Installation

git clone https://github.com/Tencent/DSpine.git
cd DSpine
pip install -e .

Usage

sglang serve \
  --model-path Qwen/Qwen3.8-27B \
  --speculative-algorithm DSPINE \
  --speculative-draft-model-path /path/to/Qwen3.8-27B-DSpine-b16 \
  --speculative-num-draft-tokens 16 \
  --dtype bfloat16 \
  --attention-backend fa3 \
  --speculative-draft-attention-backend fa3 \
  --linear-attn-backend triton \
  --enable-linear-replayssm-spec \
  --mamba-ssm-dtype float32 \
  --mamba-radix-cache-strategy extra_buffer \
  --max-mamba-cache-size 64 \
  --mem-fraction-static 0.80 \
  --context-length 131072 \
  --max-running-requests 8 \
  --cuda-graph-max-bs-decode 8 \
  --min-free-slots-delay 1 \
  --sampling-backend flashinfer \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder \
  --trust-remote-code

Acknowledgements

We thank DFlash, SpecForge, SGLang and FlashInfer. Dynamic verification references DSpark.

Citation

@article{zhang2026dspine,
  title   = {Draft in Parallel, Condition Through Depth: Adjacent Causal Injection for Speculative Decoding},
  author  = {Zhang, Haohui and Chen, Keyu and Sun, Haocheng and Gu, Weibo and Qiao, Ruizhi and Sun, Xing and Jiang, Bo},
  journal = {arXiv preprint arXiv:2609.36173},
  year    = {2026}
}

License

Apache 2.0

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