Text

Qwen3.5 27B

Alibaba's dense 27 billion parameter language model with native tool calling and MTP speculative decoding

Command to Run on Jetson Benchmark Model Details

Parameters 27B

Modalities

Text Image

Context Length 256K

License Apache 2.0

Precision

NVFP4 W4A16

Serve the model

Start server

Choose module, then engine and optional parameters on the left, then copy the serve command by clicking the button on the right.

Command

Call the model over Web API

Copy a client command below and paste it into your terminal to make a Web API request to the model you just served.

curl -s http://${JETSON_HOST}:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3.5-27B",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from openai import OpenAI

client = OpenAI(
    base_url="http://${JETSON_HOST}:8000/v1",
    api_key="not-needed",  # vLLM / llama.cpp typically do not enforce a key
)

completion = client.chat.completions.create(
    model="Qwen/Qwen3.5-27B",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(completion.choices[0].message.content)

Benchmark

Qwen3.5-27B · vLLM · NVFP4 / W4A16 · ISL 2048 / OSL 128

Engine

Concurrency

C = concurrent requests. Results will vary with image, clocks, and workload.

Model Details

View on HuggingFace

Qwen3.5 27B is a dense language model from Alibaba Cloud’s Qwen3.5 family. With 27 billion parameters, it delivers strong performance across complex reasoning, coding, and language understanding tasks.

Inputs and Outputs

Input: Text

Output: Text

Intended Use Cases

Reasoning: Advanced logical and analytical reasoning with chain-of-thought
Function Calling: Native support for tool use and function calling
Multilingual Instruction Following: Following instructions across 100+ languages
Code Generation: Programming assistance in multiple languages
Translation: High-quality translation between supported languages

Running with vLLM

sudo docker run -it --rm --pull always --runtime=nvidia --network host \
  ghcr.io/nvidia-ai-iot/vllm:latest-jetson-orin \
  vllm serve Kbenkhaled/Qwen3.5-27B-quantized.w4a16 \
    --gpu-memory-utilization 0.8 --enable-prefix-caching \
    --reasoning-parser qwen3 \
    --enable-auto-tool-choice --tool-call-parser qwen3_coder

sudo docker run -it --rm --pull always --runtime=nvidia --network host \
  ghcr.io/nvidia-ai-iot/vllm:latest-jetson-thor \
  vllm serve Kbenkhaled/Qwen3.5-27B-NVFP4 \
    --gpu-memory-utilization 0.8 --enable-prefix-caching \
    --reasoning-parser qwen3 \
    --enable-auto-tool-choice --tool-call-parser qwen3_coder

Speculative Decoding with MTP

This model supports Multi-Token Prediction (MTP) speculative decoding, which can significantly improve generation throughput. To enable it, add the following flag to your vllm serve command:

--speculative-config '{"method": "mtp", "num_speculative_tokens": 4}'

Qwen3.5 Family

Model	Parameters	Active Params	Type	Best For
Qwen3.5 35B-A3B	35B	3B	MoE	Efficient high-performance inference
Qwen3.5 27B	27B	27B	Dense	Maximum accuracy on demanding tasks

Additional Resources

Hugging Face Model - Original model weights
NVFP4 Checkpoint (Thor) - Quantized for Jetson Thor
W4A16 Checkpoint (Orin) - Quantized for Jetson Orin