Instructions to use Qwen/Qwen3.5-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen3.5-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Qwen/Qwen3.5-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Qwen/Qwen3.5-9B")
model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen3.5-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Qwen/Qwen3.5-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen3.5-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qwen/Qwen3.5-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Qwen/Qwen3.5-9B

SGLang

How to use Qwen/Qwen3.5-9B 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 "Qwen/Qwen3.5-9B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qwen/Qwen3.5-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Qwen/Qwen3.5-9B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qwen/Qwen3.5-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Qwen/Qwen3.5-9B with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen3.5-9B
```

DGX SPARK VLLM RESULTS

by RGMC98 - opened Mar 2

Discussion

RGMC98

Mar 2

Llama Benchy results with MTP 2

model	test	t/s (total)	t/s (req)	peak t/s	peak t/s (req)	ttfr (ms)	est_ppt (ms)	e2e_ttft (ms)
Qwen/Qwen3.5-9B	pp2048 (c1)	2620.55 ± 108.88	2620.55 ± 108.88			784.91 ± 33.23	783.02 ± 33.23	785.00 ± 33.22
Qwen/Qwen3.5-9B	tg128 (c1)	9.68 ± 0.24	9.68 ± 0.24	10.67 ± 0.47	10.67 ± 0.47
Qwen/Qwen3.5-9B	pp2048 (c2)	3149.17 ± 65.54	1577.58 ± 32.55			1301.05 ± 26.31	1299.16 ± 26.31	1301.10 ± 26.31
Qwen/Qwen3.5-9B	tg128 (c2)	18.51 ± 0.40	9.79 ± 0.17	22.00 ± 0.00	11.00 ± 0.00
Qwen/Qwen3.5-9B	ctx_pp @ d4096 (c1)	3172.55 ± 235.93	3172.55 ± 235.93			1300.38 ± 97.06	1298.49 ± 97.06	1300.49 ± 97.07
Qwen/Qwen3.5-9B	ctx_tg @ d4096 (c1)	9.92 ± 0.22	9.92 ± 0.22	11.00 ± 0.00	11.00 ± 0.00
Qwen/Qwen3.5-9B	pp2048 @ d4096 (c1)	1243.76 ± 6.36	1243.76 ± 6.36			1648.56 ± 8.45	1646.67 ± 8.45	1648.66 ± 8.45
Qwen/Qwen3.5-9B	tg128 @ d4096 (c1)	10.09 ± 0.02	10.09 ± 0.02	11.00 ± 0.00	11.00 ± 0.00
Qwen/Qwen3.5-9B	ctx_pp @ d4096 (c2)	3883.72 ± 15.81	1943.85 ± 7.99			2109.68 ± 8.86	2107.79 ± 8.86	2109.73 ± 8.85
Qwen/Qwen3.5-9B	ctx_tg @ d4096 (c2)	19.40 ± 0.59	10.05 ± 0.03	22.00 ± 0.00	11.00 ± 0.00
Qwen/Qwen3.5-9B	pp2048 @ d4096 (c2)	1304.04 ± 37.92	652.49 ± 18.98			3143.33 ± 93.21	3141.44 ± 93.21	3143.39 ± 93.21
Qwen/Qwen3.5-9B	tg128 @ d4096 (c2)	18.82 ± 0.91	9.91 ± 0.16	22.00 ± 0.00	11.00 ± 0.00
Qwen/Qwen3.5-9B	ctx_pp @ d8192 (c1)	3820.75 ± 15.57	3820.75 ± 15.57			2146.18 ± 8.64	2144.29 ± 8.64	2146.25 ± 8.62
Qwen/Qwen3.5-9B	ctx_tg @ d8192 (c1)	9.97 ± 0.00	9.97 ± 0.00	11.00 ± 0.00	11.00 ± 0.00
Qwen/Qwen3.5-9B	pp2048 @ d8192 (c1)	775.86 ± 3.93	775.86 ± 3.93			2641.61 ± 13.35	2639.72 ± 13.35	2641.66 ± 13.34
Qwen/Qwen3.5-9B	tg128 @ d8192 (c1)	9.89 ± 0.01	9.89 ± 0.01	10.33 ± 0.47	10.33 ± 0.47
Qwen/Qwen3.5-9B	ctx_pp @ d8192 (c2)	4077.40 ± 3.08	2039.80 ± 1.56			4018.39 ± 2.99	4016.50 ± 2.99	4018.44 ± 2.99
Qwen/Qwen3.5-9B	ctx_tg @ d8192 (c2)	18.92 ± 0.11	9.78 ± 0.11	21.33 ± 0.94	10.67 ± 0.47
Qwen/Qwen3.5-9B	pp2048 @ d8192 (c2)	816.36 ± 1.05	408.37 ± 0.53			5016.98 ± 6.46	5015.09 ± 6.46	5017.04 ± 6.46
Qwen/Qwen3.5-9B	tg128 @ d8192 (c2)	18.48 ± 0.65	9.72 ± 0.02	20.00 ± 0.00	10.00 ± 0.00
Qwen/Qwen3.5-9B	ctx_pp @ d16384 (c1)	3927.25 ± 5.82	3927.25 ± 5.82			4173.86 ± 6.41	4171.97 ± 6.41	4173.96 ± 6.40
Qwen/Qwen3.5-9B	ctx_tg @ d16384 (c1)	9.57 ± 0.02	9.57 ± 0.02	10.00 ± 0.00	10.00 ± 0.00
Qwen/Qwen3.5-9B	pp2048 @ d16384 (c1)	436.06 ± 0.63	436.06 ± 0.63			4698.53 ± 6.79	4696.64 ± 6.79	4698.64 ± 6.77
Qwen/Qwen3.5-9B	tg128 @ d16384 (c1)	9.51 ± 0.02	9.51 ± 0.02	10.00 ± 0.00	10.00 ± 0.00
Qwen/Qwen3.5-9B	ctx_pp @ d16384 (c2)	4050.36 ± 66.34	2036.83 ± 52.49			8051.44 ± 202.01	8049.55 ± 202.01	8051.50 ± 202.02
Qwen/Qwen3.5-9B	ctx_tg @ d16384 (c2)	18.58 ± 0.09	9.45 ± 0.07	20.33 ± 0.47	10.33 ± 0.47
Qwen/Qwen3.5-9B	pp2048 @ d16384 (c2)	447.29 ± 5.77	224.80 ± 4.71			9116.12 ± 187.59	9114.23 ± 187.59	9116.16 ± 187.59
Qwen/Qwen3.5-9B	tg128 @ d16384 (c2)	18.20 ± 0.03	9.35 ± 0.13	20.33 ± 0.47	10.33 ± 0.47
Qwen/Qwen3.5-9B	ctx_pp @ d32768 (c1)	3906.92 ± 2.15	3906.92 ± 2.15			8389.14 ± 4.70	8387.25 ± 4.70	8389.22 ± 4.70
Qwen/Qwen3.5-9B	ctx_tg @ d32768 (c1)	8.65 ± 0.02	8.65 ± 0.02	9.00 ± 0.00	9.00 ± 0.00
Qwen/Qwen3.5-9B	pp2048 @ d32768 (c1)	224.00 ± 1.87	224.00 ± 1.87			9145.26 ± 75.94	9143.37 ± 75.94	9145.36 ± 75.96
Qwen/Qwen3.5-9B	tg128 @ d32768 (c1)	8.61 ± 0.02	8.61 ± 0.02	9.00 ± 0.00	9.00 ± 0.00
Qwen/Qwen3.5-9B	ctx_pp @ d32768 (c2)	3712.80 ± 281.87	1874.29 ± 154.97			17612.52 ± 1538.50	17610.63 ± 1538.50	17612.58 ± 1538.51
Qwen/Qwen3.5-9B	ctx_tg @ d32768 (c2)	16.27 ± 0.66	8.64 ± 0.20	18.00 ± 0.00	9.33 ± 0.47
Qwen/Qwen3.5-9B	pp2048 @ d32768 (c2)	216.48 ± 15.33	109.29 ± 8.49			18861.48 ± 1543.33	18859.59 ± 1543.33	18861.53 ± 1543.32
Qwen/Qwen3.5-9B	tg128 @ d32768 (c2)	16.14 ± 0.76	8.55 ± 0.25	18.00 ± 0.00	9.17 ± 0.37
Qwen/Qwen3.5-9B	ctx_pp @ d65535 (c1)	3157.82 ± 119.30	3157.82 ± 119.30			20785.76 ± 803.08	20783.87 ± 803.08	20785.85 ± 803.07
Qwen/Qwen3.5-9B	ctx_tg @ d65535 (c1)	7.72 ± 0.09	7.72 ± 0.09	8.33 ± 0.47	8.33 ± 0.47
Qwen/Qwen3.5-9B	pp2048 @ d65535 (c1)	91.90 ± 0.46	91.90 ± 0.46			22286.91 ± 111.65	22285.02 ± 111.65	22286.99 ± 111.63
Qwen/Qwen3.5-9B	tg128 @ d65535 (c1)	7.64 ± 0.09	7.64 ± 0.09	8.33 ± 0.47	8.33 ± 0.47
Qwen/Qwen3.5-9B	ctx_pp @ d65535 (c2)	2286.44 ± 491.33	1155.57 ± 254.76			60172.33 ± 15685.93	60170.44 ± 15685.93	60175.67 ± 15685.28
Qwen/Qwen3.5-9B	ctx_tg @ d65535 (c2)	7.94 ± 1.11	4.42 ± 0.36	14.67 ± 0.94	7.33 ± 0.47
Qwen/Qwen3.5-9B	pp2048 @ d65535 (c2)	80.29 ± 2.96	40.45 ± 1.46			50694.61 ± 1874.98	50692.72 ± 1874.98	50698.34 ± 1874.29
Qwen/Qwen3.5-9B	tg128 @ d65535 (c2)	10.07 ± 1.40	5.35 ± 0.89	15.33 ± 0.94	7.67 ± 0.47
Qwen/Qwen3.5-9B	ctx_pp @ d100000 (c1)	2407.21 ± 33.79	2407.21 ± 33.79			41552.12 ± 578.07	41550.23 ± 578.07	41582.82 ± 599.50
Qwen/Qwen3.5-9B	ctx_tg @ d100000 (c1)	4.31 ± 0.81	4.31 ± 0.81	7.67 ± 1.25	7.67 ± 1.25
Qwen/Qwen3.5-9B	pp2048 @ d100000 (c1)	44.33 ± 4.38	44.33 ± 4.38			46680.51 ± 4856.03	46678.62 ± 4856.03	46702.83 ± 4849.40
Qwen/Qwen3.5-9B	tg128 @ d100000 (c1)	5.64 ± 0.49	5.64 ± 0.49	7.67 ± 0.94	7.67 ± 0.94
Qwen/Qwen3.5-9B	ctx_pp @ d100000 (c2)	2328.80 ± 182.23	1200.93 ± 76.42			83646.13 ± 5896.07	83644.24 ± 5896.07	83653.27 ± 5892.10
Qwen/Qwen3.5-9B	ctx_tg @ d100000 (c2)	5.98 ± 4.08	4.48 ± 2.12	11.67 ± 4.78	7.06 ± 2.53
Qwen/Qwen3.5-9B	pp2048 @ d100000 (c2)	48.26 ± 0.91	24.32 ± 0.50			84264.99 ± 1754.30	84263.10 ± 1754.30	84269.39 ± 1752.62
Qwen/Qwen3.5-9B	tg128 @ d100000 (c2)	8.28 ± 1.01	4.56 ± 0.57	14.67 ± 0.94	7.33 ± 0.47

hebangwen

Mar 3

The generation speed appears to be quite low, at only 9.68 to 18.51 t/s. It's not reasonable for a 9B model that supports linear attention. Does this indicate that there is still room for optimization?

kth8

Mar 5

On a system with large amount of low bandwidth memory, going with MoE option is the obvious better choice

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