Instructions to use Qwen/Qwen3.8-Flash-Next with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen3.8-Flash-Next with Transformers:

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

pipe = pipeline("image-text-to-text", model="Qwen/Qwen3.8-Flash-Next")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Qwen/Qwen3.8-Flash-Next")
model = AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen3.8-Flash-Next", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Qwen/Qwen3.8-Flash-Next with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen3.8-Flash-Next"
# 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/Qwen3.8-Flash-Next",
		"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/Qwen3.8-Flash-Next

SGLang

How to use Qwen/Qwen3.8-Flash-Next 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/Qwen3.8-Flash-Next" \
    --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/Qwen3.8-Flash-Next",
		"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/Qwen3.8-Flash-Next" \
        --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/Qwen3.8-Flash-Next",
		"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/Qwen3.8-Flash-Next with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen3.8-Flash-Next
```

Qwen3.8-Flash-Next vs Qwen 3.8 27B - Architecture Teardown

#22

by laxmimerit - opened about 3 hours ago

Discussion

laxmimerit

about 3 hours ago

I could not run this (360 GB against a 32 GB card), so I read config.json and
model.safetensors.index.json for Flash Next and for Qwen 3.8 27B and compared them.
Everything below is counted from those two files, not read off the model card.

Most of the model is untouched: same 248,320 vocab and bos/eos id, same 3 linear to 1
full attention pattern, head_dim 256, 24 query heads, rope_theta 1e7, 262,144
context, same vision tower. The stack is smaller, 48 blocks at 2,560 wide against 64
at 5,120. Four parts changed.

Part	Qwen 3.8 27B	Qwen3.8-Flash-Next
Feed forward	dense MLP, 17,408 wide, all active	512 experts of 640, 10 routed + 1 shared. 121B stored, 2.6B run
Attention	16 full blocks, whole cache, KV 64 KiB/tok	12 blocks behind a DSA indexer (`indexer_budget` 2048), `num_key_value_heads` 2, KV 24.75 KiB/tok
Normalization	`input_layernorm` + `post_attention_layernorm`	hyper connections, `hc_count` 4, `hc_lowrank` 320, four residual lanes at 10,240
Extra capacity	none	PLE at layer 2, `ngram_size` 3, 128 shards, 51.2B params with no matmul

Per decoded token at 262k: KV read is 16.0 GiB against roughly 240 MiB, and total
traffic at bf16 is 66.4 GiB against 11.5 GiB. But this is not constant cost attention.
The indexer still scans at 768 bytes per token, so the curve rises, just 85 times more
slowly.

Two questions:

What separates DeepSWE from SWE-bench Pro? DeepSWE gains 16.5 points and
SWE-bench Pro gains 0.8, and the card says both ran at 256k. If depth were the whole
mechanism, those should move together.

Is anyone working on host side PLE offload? The 51B table is a third of the
storage and a pure lookup, so it is the one part that suits system RAM. That looks
like the difference between needing 100 GB of VRAM and a machine people already own.

Full teardown: https://kgptalkie.com/tutorials/generative-ai/qwen-3-8-flash-next-vs-qwen-3-8-27b

seamon67

about 2 hours ago

how about offloading the 51B to a fast SSD?

laxmimerit

about 2 hours ago

how about offloading the 51B to a fast SSD?

Yes that is possible. Upcoming Qwen 4 seems quite promising.

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