Qwen3-VL

Qwen3-VL-32B-Instruct is a 32B-parameter dense vision-language model.

Multimodal (image)Tool calling

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

Install the Baseten CLI and sign in, then install the OpenAI SDK.

Install and sign in to Baseten

macOS or Linux

Terminal

brew tap basetenlabs/baseten
brew install baseten

Windows

Download and extract the binary, then move baseten.exe to a directory on your PATH:

Terminal

Invoke-WebRequest `
  https://github.com/basetenlabs/baseten-cli/releases/download/v0.4.0/baseten_0.4.0_windows_amd64.zip `
  -OutFile baseten.zip; Expand-Archive -Force baseten.zip .

For other platforms or a specific version, see the Baseten CLI install reference.

baseten auth login

Install the OpenAI SDK

uv pip install openai

Prefer not to install? Sign in with uvx truss login --browser and deploy with uvx truss push.

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.

Hardware

RTX_PRO_6000

Engine

vLLM (0.22.0-cu129 build)

Concurrency

16

Write the config

Create and move into the project directory:

mkdir qwen3-vl-32b-throughput && cd qwen3-vl-32b-throughput

Then create a file named config.yaml and paste the following:

config.yaml

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 using 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: 16

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 with the Baseten CLI, or with the Truss CLI if you prefer it:

Baseten CLI

baseten model push

You should see output similar to:

Pushing model "qwen3-vl-32b-throughput"...
Uploading model...
Uploaded model in 0s
✨ Model qwen3-vl-32b-throughput was successfully pushed ✨

  Model:       qwen3-vl-32b-throughput (abc1d2ef)
  Deployment:  xyz123
  Environment: production

🪵 View logs:
   deployment:   baseten model deployment logs --model-id abc1d2ef --deployment-id xyz123
   environment:  baseten model environment logs --model-id abc1d2ef --environment production  (once deployed)
   app:          https://app.baseten.co/models/abc1d2ef/logs/xyz123

🚀 Invoke your model:
   URL:  https://model-abc1d2ef.api.baseten.co/deployment/xyz123/predict
   CLI:  baseten model predict --model-id abc1d2ef

baseten model push prints your model ID (abc1d2ef in the example). The examples below use it wherever you see {model_id}, and read your API key from the BASETEN_API_KEY environment variable.

Call the model

Your deployment serves an OpenAI-compatible API.

Now call your deployment to run inference:

Python

main.py

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)

cURL

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:

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)

Next steps

Call your model

Endpoint anatomy, authentication, and sync versus async inference

Autoscaling

Scale replicas with traffic, including scale to zero