Instructions to use RadixArk/Qwen3.8-27B-DSpark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RadixArk/Qwen3.8-27B-DSpark with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RadixArk/Qwen3.8-27B-DSpark", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("RadixArk/Qwen3.8-27B-DSpark", trust_remote_code=True)
model = AutoModel.from_pretrained("RadixArk/Qwen3.8-27B-DSpark", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RadixArk/Qwen3.8-27B-DSpark with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RadixArk/Qwen3.8-27B-DSpark"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RadixArk/Qwen3.8-27B-DSpark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RadixArk/Qwen3.8-27B-DSpark

SGLang

How to use RadixArk/Qwen3.8-27B-DSpark with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RadixArk/Qwen3.8-27B-DSpark" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RadixArk/Qwen3.8-27B-DSpark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RadixArk/Qwen3.8-27B-DSpark" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RadixArk/Qwen3.8-27B-DSpark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RadixArk/Qwen3.8-27B-DSpark with Docker Model Runner:
```
docker model run hf.co/RadixArk/Qwen3.8-27B-DSpark
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.8-27B-DSpark

A DSpark speculative-decoding draft model for Qwen3.8-27B target models, trained with SpecForge and served with SGLang.

The checkpoint has been evaluated with both RadixArk/Qwen3.8-27B-NVFP4 and Qwen/Qwen3.8-27B-FP8 targets. The acceptance-length evaluation below uses the NVFP4 target. The throughput evaluation uses the FP8 target.

Checkpoint

Draft parameters: 1,857,358,337 (1.86B)
Draft weight dtype: BF16
Hidden size: 5,120
Transformer layers: five full-attention layers
Attention: GQA with 32 query heads and eight key/value heads
Target auxiliary feature layers: 5, 19, 33, 47, 61
Markov head: VanillaMarkov, rank 256
Training target width: 16 future positions
Serving gamma: seven draft proposals
Target verification width: eight tokens, including the target bonus token
Maximum position embeddings: 262,144

The serving configuration uses block_size=7. The separate training_block_size=16 records the supervision width used during training.

Acceptance length

Results cover 64,675 completed requests across 17 workloads.

Category	Workload	Prompts	DSpark v1	DSpark v2
Code	HumanEval	164	3.0437	3.8468
Code	MBPP	257	3.2299	4.0603
Code	LiveCodeBench	1,055	2.5915	3.3462
Code	BigCodeBench	1,140	2.7752	3.4678
Math	GSM8K	1,319	3.6030	4.5162
Math	MATH-500	500	3.2559	4.2267
Math	AIME 2025	30	2.9798	3.9401
Math	AMC23	40	3.2111	4.1572
Math	GSM-Symbolic	2,048	3.4554	4.2716
Chat	MT-Bench	80	2.6075	3.2860
Chat	Alpaca	52,002	2.5659	3.2337
Chat	Arena-Hard-v2	750	2.5910	3.2536
Chat	IFEval	541	2.9457	3.6628
Misc.	MMLU-Pro	2,048	2.8345	3.5964
Misc.	GPQA-Diamond	198	2.7634	3.5109
Misc.	LongBench-v2	503	3.2602	3.9268
Misc.	RULER-8K	2,000	4.9585	6.3009

Aggregate	DSpark v1	DSpark v2	Change
Request-count weighted, 64,675 prompts	2.721143	3.428567	+26.00%
Workload macro, 17 workloads	3.098368	3.917881	+26.45%

Acceptance-length protocol:

Runtime: SGLang v0.5.17
Hardware and topology: four NVIDIA GB300 GPUs, DP4 × TP1
Sampling: thinking enabled, temperature 1.0, top-p 0.95, top-k 20, seed 980406
Generation limit: 8,192 tokens; client concurrency: 128

Throughput

Throughput is total output tokens divided by end-to-end timed wall duration. Each speculative-decoding cell is output tok/s (speedup over autoregressive).

Concurrency 1

Workload	Autoregressive	EAGLE	DSpark v1	DSpark v2
GSM8K	94.2	179.9 (1.91×)	238.6 (2.53×)	297.3 (3.16×)
MATH-500	95.0	174.0 (1.83×)	214.4 (2.26×)	280.0 (2.95×)
HumanEval	95.8	165.5 (1.73×)	205.5 (2.14×)	254.8 (2.66×)
MBPP	93.8	166.7 (1.78×)	208.6 (2.22×)	261.6 (2.79×)
MT-Bench	95.8	157.4 (1.64×)	171.3 (1.79×)	215.8 (2.25×)

Concurrency 8

Workload	Autoregressive	EAGLE	DSpark v1	DSpark v2
GSM8K	602.7	1,001.1 (1.66×)	1,183.8 (1.96×)	1,494.0 (2.48×)
MATH-500	635.2	1,071.6 (1.69×)	1,208.2 (1.90×)	1,575.1 (2.48×)
HumanEval	667.9	1,031.3 (1.54×)	1,159.2 (1.74×)	1,435.1 (2.15×)
MBPP	635.4	988.3 (1.56×)	1,123.7 (1.77×)	1,393.7 (2.19×)
MT-Bench	647.9	963.2 (1.49×)	958.4 (1.48×)	1,195.5 (1.85×)

Concurrency 32

Workload	Autoregressive	EAGLE	DSpark v1	DSpark v2
GSM8K	1,298.5	1,969.5 (1.52×)	1,934.2 (1.49×)	2,268.5 (1.75×)
MATH-500	1,764.2	2,353.4 (1.33×)	2,014.2 (1.14×)	2,545.2 (1.44×)
HumanEval	1,862.2	2,296.9 (1.23×)	1,918.5 (1.03×)	2,472.3 (1.33×)
MBPP	1,738.4	2,286.3 (1.32×)	1,926.3 (1.11×)	2,413.1 (1.39×)
MT-Bench	1,814.2	2,133.4 (1.18×)	1,593.3 (0.88×)	1,973.0 (1.09×)

Throughput protocol:

EAGLE uses the target-integrated MTP head loaded as Qwen3_5ForCausalLMMTP, without an external draft checkpoint
Hardware and topology: one NVIDIA H200 per workload, TP1 × DP1
128 prompts per cell, dataset shuffle seed 42, concurrency 1/8/32, max_tokens=2048, reasoning effort xhigh, temperature 1.0, top-p 0.95, top-k 20
EAGLE serving: three speculative steps, top-k 1, four draft tokens, Mamba full-memory ratio 8.26, extra_buffer radix-cache strategy, float32 Mamba state
DSpark serving: gamma 7, target verify width 8, one speculative step, block size 7, Mamba full-memory ratio 11.93, extra_buffer radix-cache strategy, float32 Mamba state
Autoregressive and EAGLE serving used mem-fraction-static=0.85. DSpark used 0.80 with expandable CUDA allocation segments. Every mode retained its complete prefill and speculative-verification CUDA graph set and used max-running-requests=48.

Serving with SGLang

PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
SGLANG_RAGGED_VERIFY_MODE=static \
sglang serve \
  --trust-remote-code \
  --model-path Qwen/Qwen3.8-27B-FP8 \
  --kv-cache-dtype fp8_e4m3 \
  --mem-fraction-static 0.80 \
  --attention-backend flashinfer \
  --chunked-prefill-size 32768 \
  --max-prefill-tokens 32768 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder \
  --mamba-full-memory-ratio 11.93 \
  --mamba-radix-cache-strategy extra_buffer \
  --mamba-ssm-dtype float32 \
  --max-running-requests 48 \
  --speculative-algorithm DSPARK \
  --speculative-draft-model-path RadixArk/Qwen3.8-27B-DSpark \
  --speculative-draft-model-quantization unquant \
  --speculative-draft-attention-backend flashinfer \
  --speculative-dspark-block-size 7 \
  --speculative-num-steps 1 \
  --speculative-eagle-topk 1 \
  --host 127.0.0.1 \
  --port 30000

Artifact identity

File	Bytes	SHA-256
`model.safetensors`	3,714,723,322	`2aff025f45823b40ebe726b9dfa40302f3512bd9a11c3a7347de32a567acd9a7`
`config.json`	2,448	`dd65fb1b01c2adea69512ff2990a79d58eb7fe2c7ea97375aa66f657a29a5bfd`