> ## 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.
Meta's compact Llama 3.2 instruction-tuned model. Runs on a single H100 40GB for low-cost chat and edge-adjacent workloads.
## 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 trust --formula basetenlabs/baseten/baseten
brew install baseten
```
Download and extract the Windows archive for the latest Baseten CLI release:
```powershell Terminal theme={"system"}
$release = Invoke-RestMethod https://api.github.com/repos/basetenlabs/baseten-cli/releases/latest
$asset = $release.assets | Where-Object { $_.name -like "*_windows_amd64.zip" }
Invoke-WebRequest $asset.browser_download_url -OutFile baseten.zip
Expand-Archive -Force baseten.zip .
```
Move `baseten.exe` to a directory on your `PATH`.
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
```
To install without Homebrew, see the [Baseten CLI install reference](/reference/cli/baseten/overview#install).
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: py311
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 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 "llama-3.2-3b-instruct-latency"...
Uploading model...
Uploaded model in 0s
✨ Model llama-3.2-3b-instruct-latency was successfully pushed ✨
Model: llama-3.2-3b-instruct-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="",
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?"}
]
}'
```
## Next steps
Endpoint anatomy, authentication, and sync versus async inference
Scale replicas with traffic, including scale to zero