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To get the best performance, we recommend using our TensorRT-LLM Engine-Builder when deploying LLMs. Models deployed with the Engine-Builder are OpenAI compatible, support structured output and function calling, and offer deploy-time post-training quantization to FP8 with Hopper GPUs and NVFP4 with Blackwell GPUs. The Engine-Builder supports LLMs from the following families, both foundation models and fine-tunes:
  • Llama 3.0 and later (including DeepSeek-R1 distills)
  • Qwen 2.5 and later (including Math, Coder, and DeepSeek-R1 distills)
  • Mistral (all LLMs)
You can find preset Engine-Builder configs for common models in the Engine-Builder reference.
The Engine-Builder does not support vision-language models like Llama 3.2 11B or Pixtral. For these models, we recommend vLLM.

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Example: Deploy Qwen 2.5 3B on an H100

This configuration builds an inference engine to serve Qwen 2.5 3B on an H100 GPU. Running this model is fast and cheap, making it a good example for documentation, but the process of deploying it is very similar to larger models like GLM-4.7.

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Setup

This guide uses uvx to run Truss commands without a separate install step. Sign in to Baseten and install the OpenAI SDK. Browser login opens a tab to approve this device, so there’s no API key to copy and paste.
Sign in to Baseten
uvx truss login --browser
Install the OpenAI SDK
uv pip install openai
Hugging Face access for gated models. Some models require that you accept terms and conditions on Hugging Face before deployment. To prevent issues:
  1. Accept the license for any gated models you wish to access, like Gemma 3.
  2. Create a read-only user access token from your Hugging Face account.
  3. Add the hf_access_token secret to your Baseten workspace.

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Configuration

Start with an empty configuration file. 
mkdir qwen-2-5-3b-engine
touch qwen-2-5-3b-engine/config.yaml
This configuration file specifies model information and Engine-Builder arguments. You can find details on each config option in the Engine-Builder reference. Below is an example for Qwen 2.5 3B.
config.yaml
model_metadata:
  tags:
    - openai-compatible
  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: true
    max_tokens: 512
    temperature: 0.6  # Check recommended temperature per model
  repo_id: Qwen/Qwen2.5-3B-Instruct
model_name: Qwen 2.5 3B Instruct
python_version: py39
resources: # Engine-Builder GPU cannot be changed post-deployment
  accelerator: H100
  use_gpu: true
secrets: {}
trt_llm:
  build:
    base_model: decoder 
    checkpoint_repository:
      repo: Qwen/Qwen2.5-3B-Instruct
      source: HF
    num_builder_gpus: 1
    quantization_type: no_quant # `fp8_kv` often recommended for large models
    max_seq_len: 32768 # vary the max sequence length, for example 131072 for Llama models
    tensor_parallel_count: 1 # Set equal to number of GPUs
    plugin_configuration:
      use_paged_context_fmha: true
      use_fp8_context_fmha: false # Set to true when using `fp8_kv`
      paged_kv_cache: true
  runtime:
    batch_scheduler_policy: max_utilization
    enable_chunked_context: true
    request_default_max_tokens: 32768 # 131072 for Llama models

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Deployment

Pushing the model to Baseten kicks off a multi-stage build and deployment process. 
uvx truss push qwen-2-5-3b-engine
Upon deployment, check your terminal logs or Baseten account to find the URL for the model server.

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Inference

This model is OpenAI compatible and can be called using the OpenAI client. 
import os
from openai import OpenAI

# https://model-XXXXXXX.api.baseten.co/environments/production/sync/v1
model_url = ""

client = OpenAI(
    base_url=model_url,
    api_key=os.environ.get("BASETEN_API_KEY"),
)

stream = client.chat.completions.create(
    model="baseten",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What does Tongyi Qianwen mean?"}
    ],
    stream=True,
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")
That’s it! You have successfully deployed and called an LLM optimized with the TensorRT-LLM Engine-Builder. Check the Engine-Builder reference for details on each config option.