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Deploying a model to Baseten turns a Hugging Face model into a production-ready API endpoint. You write a config.yaml that specifies the model, the hardware, and the engine, then uvx truss push builds a TensorRT-optimized container and deploys it. No Python code, no Dockerfile, no container management. This guide walks through deploying Qwen 2.5 3B Instruct, a small but capable LLM, from a config file to a production API. You’ll set up Truss, write a config, deploy to Baseten, and call the model’s OpenAI-compatible endpoint.

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Install and sign in

Before you begin, sign up or sign in to Baseten, then install uv, a fast Python package manager. Install the Truss CLI and connect it to your Baseten account. Browser login opens a tab to approve this device, so there’s no API key to copy and paste.
Install Truss
uv tool install truss
Sign in
truss login --browser
Prefer not to install? Run uvx truss login --browser to use the same flow without a permanent install, and use uvx truss … for the rest of this guide.

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Create a Truss project

Create a directory for your project: 
mkdir qwen-2.5-3b && cd qwen-2.5-3b
TRT-LLM engine deployments only need a config.yaml. No custom Python code is required, and the model/ directory (used for custom preprocessing or postprocessing) isn’t needed here.

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Write the config

Create a config.yaml with:
config.yaml
model_metadata:
  tags:
    - openai-compatible
model_name: Qwen-2.5-3B
resources:
  accelerator: L4
  use_gpu: true
trt_llm:
  build:
    base_model: decoder
    checkpoint_repository:
      source: HF
      repo: "Qwen/Qwen2.5-3B-Instruct"
    max_seq_len: 8192
    quantization_type: fp8
    tensor_parallel_count: 1
    num_builder_gpus: 2
That’s the entire deployment specification.
  • model_name identifies the model in your Baseten dashboard.
  • resources selects an L4 GPU (24 GB VRAM), which is plenty for a 3B parameter model.
  • trt_llm tells Baseten to use Engine-Builder-LLM, which compiles the model with TensorRT-LLM for optimized inference.
  • checkpoint_repository points to the model weights on Hugging Face. Qwen 2.5 3B Instruct is ungated, so no access token is needed.
  • quantization_type: fp8 compresses weights to 8-bit floating point, cutting memory usage roughly in half with negligible quality loss.
  • max_seq_len: 8192 sets the maximum context length for requests.
  • num_builder_gpus: 2 uses two GPUs during the build phase. FP8 quantization requires more GPU memory at build time than at inference time, so a single L4 runs out of memory during compilation without this setting.

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Deploy

Push the model to Baseten:
Engine-based deployments (TRT-LLM) use published deployments by default. The --watch flag, which creates a development deployment with live reload, is not supported for TRT-LLM models. For custom Python models, see Customize a model where --watch enables a faster development loop.
truss push
You should see: 
✨ Model Qwen 2.5 3B was successfully pushed ✨

   Model ID:      abc1d2ef
   Deployment ID: xyz123
   Endpoint:      model-abc1d2ef.api.baseten.co
   Logs:          https://app.baseten.co/models/abc1d2ef/logs/xyz123
Baseten downloads the model weights from Hugging Face, compiles them with TensorRT-LLM, and deploys the resulting container to an L4 GPU. This build step takes roughly 10-20 minutes for the first deploy. You can watch progress in the logs linked above.

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Call the model

Engine-based deployments serve an OpenAI-compatible API. Once the deployment shows “Active” in the dashboard, call it using the OpenAI SDK or cURL. Replace {model_id} with your model ID from the deployment output. The endpoint follows this shape: Anatomy of the model API endpoint. In https://model-abc123.api.baseten.co/environments/production/sync/v1, abc123 is the model ID and production is the environment that serves the request.
Install the OpenAI SDK if you don’t have it:
uv pip install openai
Create a chat completion:
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-2.5-3B",
    messages=[
        {"role": "user", "content": "What is machine learning?"}
    ],
)

print(response.choices[0].message.content)
You should see a response like: 
Machine learning is a branch of artificial intelligence where systems learn
patterns from data to make predictions or decisions without being explicitly
programmed for each task...
Any code that works with the OpenAI SDK works with your deployment. Just point the base_url at your model’s endpoint.

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Next steps

Engine configuration

Tune max sequence length, batch size, quantization, and runtime settings.

Customize a model

Add custom Python code when you need preprocessing, postprocessing, or unsupported model architectures.

Autoscaling

Configure replicas, concurrency targets, and scale-to-zero for production traffic.