> ## 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.
# Llama 3.2
> Meta's compact Llama 3.2 instruction-tuned model. Runs on a single H100 40GB for low-cost chat and edge-adjacent workloads.
## 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
```
[meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) is a 3B-parameter dense model with up to 125K context.
This preset serves Llama 3.2 3B Instruct on a single H100 40GB through [Baseten Inference Stack](/engines/bis-llm/overview) (TensorRT-LLM), optimized for the lowest Llama 3.2 latency on Baseten.
H100\_40GB
TRT-LLM
128K
## Write the config
Create and move into the project directory:
```sh theme={"system"}
mkdir llama-3.2-3b-instruct-latency && cd llama-3.2-3b-instruct-latency
```
Then create a file named `config.yaml` and paste the following:
```yaml config.yaml theme={"system"}
model_metadata:
example_model_input:
max_tokens: 512
messages:
- content: Tell me everything you know about optimized inference.
role: user
stream: true
temperature: 0.5
tags:
- openai-compatible
model_name: "model:llama-3.2-3b-instruct preset:latency"
python_version: py39
resources:
accelerator: H100_40GB
cpu: "1"
memory: 10Gi
use_gpu: true
trt_llm:
build:
base_model: decoder
checkpoint_repository:
repo: meta-llama/Llama-3.2-3B-Instruct
revision: main
source: HF
max_seq_len: 131072
quantization_type: fp8_kv
tensor_parallel_count: 1
runtime:
enable_chunked_context: true
```
## 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 |
| ------------------- | --------- |
| Max sequence length | `131072` |
| Chunked prefill | `enabled` |
| Quantization | `fp8_kv` |
| Base model type | `decoder` |
## Deploy
Push the config to Baseten:
```sh theme={"system"}
uvx truss push
```
You should see output similar to:
```text theme={"system"}
✨ Model llama-3.2-3b-instruct-latency was successfully pushed ✨
🪵 View logs for your deployment at https://app.baseten.co/models/abcd1234/logs/wxyz5678
```
Your **model ID** is the string after `/models/` in the logs URL (`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="",
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": "",
"messages": [
{"role": "user", "content": "What is machine learning?"}
]
}'
```