> ## Documentation Index
> Fetch the complete documentation index at: https://docs.baseten.co/llms.txt
> Use this file to discover all available pages before exploring further.
# Qwen3-VL
> Qwen3-VL-32B-Instruct is a 32B-parameter dense vision-language model. This recipe serves the RedHatAI NVFP4 quantization with image input and native tool calling.
## Setup
To get started, sign into Baseten with Truss and then install the OpenAI SDK.
**Sign in to Baseten**
```sh theme={"system"}
uvx truss login --browser
```
**Install the OpenAI SDK**
```sh theme={"system"}
uv pip install openai
```
[RedHatAI/Qwen3-VL-32B-Instruct-NVFP4](https://huggingface.co/RedHatAI/Qwen3-VL-32B-Instruct-NVFP4) is a 32B-parameter dense model.
This preset serves the RedHatAI NVFP4 quantization of Qwen3-VL-32B-Instruct on a single RTX PRO 6000 Blackwell GPU, optimized for throughput on vision-language workloads.
RTX\_PRO\_6000
vLLM (0.22.0-cu129 build)
8
## Write the config
Create and move into the project directory:
```sh theme={"system"}
mkdir qwen3-vl-32b-throughput && cd qwen3-vl-32b-throughput
```
Then create a file named `config.yaml` and paste the following:
```yaml config.yaml theme={"system"}
model_name: "model:qwen3-vl-32b preset:throughput"
model_metadata:
description: >-
Qwen3-VL-32B-Instruct (NVFP4), an OpenAI-compatible multimodal chat model with
vision served via vLLM.
repo_id: RedHatAI/Qwen3-VL-32B-Instruct-NVFP4
example_model_input:
model: Qwen/Qwen3-VL-32B-Instruct
messages:
- role: user
content:
- type: text
text: "Describe this image in one sentence."
- type: image_url
image_url:
url: "https://picsum.photos/id/237/200/300"
stream: true
max_tokens: 512
temperature: 1.0
tags:
- openai-compatible
base_image:
image: vllm/vllm-openai:v0.22.0-cu129
weights:
- source: "hf://RedHatAI/Qwen3-VL-32B-Instruct-NVFP4@main"
mount_location: "/app/checkpoint/model"
auth_secret_name: "hf_access_token"
secrets:
hf_access_token: null
docker_server:
start_command: >-
sh -c "GPU_COUNT=$(nvidia-smi --list-gpus | wc -l) && vllm serve /app/checkpoint/model
--tensor-parallel-size $GPU_COUNT
--served-model-name Qwen/Qwen3-VL-32B-Instruct
--max-num-seqs 16
--max-model-len auto
--limit-mm-per-prompt.image 2
--gpu-memory-utilization 0.9
--enable-prefix-caching
--trust-remote-code
--enable-auto-tool-choice
--tool-call-parser hermes
--load-format runai_streamer"
readiness_endpoint: /health
liveness_endpoint: /health
predict_endpoint: /v1/chat/completions
server_port: 8000
environment_variables:
VLLM_LOGGING_LEVEL: WARNING
VLLM_ENGINE_READY_TIMEOUT_S: "3600"
resources:
accelerator: RTX_PRO_6000
use_gpu: true
runtime:
health_checks:
restart_check_delay_seconds: 1800
restart_threshold_seconds: 1200
stop_traffic_threshold_seconds: 120
predict_concurrency: 8
```
## Flags
The `start_command` passes these flags to the engine. Each one controls a runtime or serving behavior:
| Flag | Value | What it does |
| ----------------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
| `--tensor-parallel-size` | `$GPU_COUNT` | Number of GPUs to shard the model across. |
| `--max-num-seqs` | `16` | Maximum number of concurrent sequences in the batch. |
| `--max-model-len` | `auto` | Maximum context length (tokens) the server accepts per request. |
| `--limit-mm-per-prompt.image` | `2` | Maximum number of image inputs per prompt. |
| `--gpu-memory-utilization` | `0.9` | Fraction of GPU memory vLLM may use for weights and KV cache. |
| `--enable-prefix-caching` | (no value) | Reuse KV cache across requests that share a prefix. |
| `--trust-remote-code` | (no value) | Execute model-specific Python from the checkpoint (required for many Qwen, Phi, and custom architectures). |
| `--enable-auto-tool-choice` | (no value) | Let the model choose when to call tools without requiring `tool_choice: "required"`. |
| `--tool-call-parser` | `hermes` | Server-side parser that emits structured `tool_calls` on the response. **hermes:** Hermes-style function calls. |
| `--load-format` | `runai_streamer` | Weight loading backend. **runai\_streamer:** Stream weights from object storage without materializing to disk. |
## Deploy
Push the config to Baseten:
```sh theme={"system"}
uvx truss push
```
You should see output similar to:
```output theme={"system"}
✨ Model qwen3-vl-32b-throughput was successfully pushed ✨
Model ID: abc1d2ef
Deployment ID: xyz123
Endpoint: model-abc1d2ef.api.baseten.co
Logs: https://app.baseten.co/models/abc1d2ef/logs/xyz123
```
Your **model ID** is printed in the `truss push` output (`abcd1234` in the example). Use it wherever you see `{model_id}` in the next section.
## Call the model
Your deployment serves an OpenAI-compatible API. Replace `{model_id}` with your model ID and make sure `BASETEN_API_KEY` is set.
Now call your deployment to run inference:
```python main.py theme={"system"}
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["BASETEN_API_KEY"],
base_url="https://model-{model_id}.api.baseten.co/environments/production/sync/v1",
)
response = client.chat.completions.create(
model="Qwen/Qwen3-VL-32B-Instruct",
messages=[
{"role": "user", "content": "What is machine learning?"}
],
)
print(response.choices[0].message.content)
```
```sh theme={"system"}
curl -s https://model-{model_id}.api.baseten.co/environments/production/sync/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $BASETEN_API_KEY" \
-d '{
"model": "Qwen/Qwen3-VL-32B-Instruct",
"messages": [
{"role": "user", "content": "What is machine learning?"}
]
}'
```
To let the model call tools, pass a `tools` array. The server returns structured `tool_calls` on the response:
```python theme={"system"}
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"parameters": {
"type": "object",
"properties": {"location": {"type": "string"}},
"required": ["location"],
},
},
}]
response = client.chat.completions.create(
model="Qwen/Qwen3-VL-32B-Instruct",
messages=[
{"role": "user", "content": "What's the weather in Paris?"}
],
tools=tools,
)
print(response.choices[0].message.tool_calls)
```