> ## 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
> Sparse MoE model with 235B total parameters (22B active per token).
Sparse MoE model with 235B total parameters (22B active per token). FP8-quantized checkpoint for production-scale reasoning and agentic workflows.
## Setup
Install the Baseten CLI and sign in, then install the OpenAI SDK.
**Install and sign in to Baseten**
```bash Terminal theme={"system"}
brew tap basetenlabs/baseten
brew install baseten
```
Download and extract the binary, then move `baseten.exe` to a directory on your `PATH`:
```powershell Terminal theme={"system"}
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](/reference/cli/baseten/overview#install).
```sh theme={"system"}
baseten auth login
```
**Install the OpenAI SDK**
```sh theme={"system"}
uv pip install openai
```
Prefer not to install? Sign in with `uvx truss login --browser` and deploy with `uvx truss push`.
This preset serves Qwen3-235B FP8 on H100:8 with TensorRT-LLM, optimized for low time-to-first-token on single-request reasoning at this scale.
H100 × 8
TRT-LLM v2
256K
256
## Write the config
Create and move into the project directory:
```sh theme={"system"}
mkdir qwen3-235b-latency && cd qwen3-235b-latency
```
Then create a file named `config.yaml` and paste the following:
```yaml config.yaml theme={"system"}
model_metadata:
example_model_input: # Loads sample request into Baseten playground
messages:
- role: system
content: "You are a helpful assistant."
- role: user
content: "What does Tongyi Qianwen mean?"
stream: false
model: "Qwen/Qwen3-235B-A22B-Instruct-2507-FP8"
max_tokens: 512
temperature: 0.6
tags:
- openai-compatible
repo_id: Qwen/Qwen3-235B-A22B-Instruct-2507-FP8
model_name: "model:qwen3-235b preset:latency"
weights:
- source: "hf://Qwen/Qwen3-235B-A22B-Instruct-2507-FP8@main"
mount_location: "/app/model_cache/trt_model"
resources:
accelerator: H100:8
cpu: "1"
memory: 10Gi
use_gpu: true
trt_llm:
build:
checkpoint_repository:
repo: michaelfeil/empty-model
revision: main
source: HF
inference_stack: v2
runtime:
enable_chunked_prefill: true
max_batch_size: 256
max_num_tokens: 8192
max_seq_len: 262144
served_model_name: Qwen/Qwen3-235B-A22B-Instruct-2507-FP8
tensor_parallel_size: 8
patch_kwargs:
disable_overlap_scheduler: True
model_path: /app/model_cache/trt_model
moe_expert_parallel_size: 8
cuda_graph_config:
enable_padding: true
max_batch_size: 256
enable_autotune: false
guided_decoding_backend: "xgrammar"
enable_iter_perf_stats: 0
kv_cache_config:
enable_block_reuse: true
free_gpu_memory_fraction: 0.8
version_overrides:
v2_llm_version: null
```
## Key parameters
[Baseten Inference Stack](/engines/bis-llm/overview) (BIS) reads these fields from the `trt_llm` block. Each one shapes how the engine is built and served:
| Parameter | Value |
| -------------------- | ---------------------------------------- |
| Tensor parallel size | `8` |
| Max sequence length | `262144` |
| Max batch size | `256` |
| Max batched tokens | `8192` |
| Chunked prefill | `enabled` |
| Inference stack | `v2` |
| Served model name | `Qwen/Qwen3-235B-A22B-Instruct-2507-FP8` |
## Deploy
Push the config to Baseten with the Baseten CLI, or with the Truss CLI if you prefer it:
```sh Baseten CLI theme={"system"}
baseten model push
```
```sh Truss CLI theme={"system"}
uvx truss push
```
You should see output similar to:
```output theme={"system"}
Pushing model "qwen3-235b-latency"...
Uploading model...
Uploaded model in 0s
✨ Model qwen3-235b-latency was successfully pushed ✨
Model: qwen3-235b-latency (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 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/Qwen3-235B-A22B-Instruct-2507-FP8",
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/Qwen3-235B-A22B-Instruct-2507-FP8",
"messages": [
{"role": "user", "content": "What is machine learning?"}
]
}'
```
## Next steps
Endpoint anatomy, authentication, and sync versus async inference
Scale replicas with traffic, including scale to zero