> ## 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 3.1
> Meta's Llama 3.1 8B instruction-tuned model. Runs on a single B200 from NVIDIA's FP8 checkpoint with EAGLE3 speculative decoding for high concurrent throughput.
## 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
```
[nvidia/Llama-3.1-8B-Instruct-FP8](https://huggingface.co/nvidia/Llama-3.1-8B-Instruct-FP8) is an 8B-parameter dense model with up to 128K context.
This preset serves Llama 3.1 8B Instruct on a single B200 through [Baseten Inference Stack](/engines/bis-llm/overview) (TensorRT-LLM) with FP8 weights, an FP8 KV cache, and EAGLE3 speculative decoding. It targets high concurrent throughput.
B200
TRT-LLM v2
128K
512
## Write the config
Create and move into the project directory:
```sh theme={"system"}
mkdir llama-3.1-8b-instruct-throughput && cd llama-3.1-8b-instruct-throughput
```
Then create a file named `config.yaml` and paste the following:
```yaml config.yaml theme={"system"}
model_name: "model:llama-3.1-8b-instruct preset:throughput"
model_metadata:
example_model_input:
messages:
- role: user
content: "Write FizzBuzz in Python"
stream: true
model: "nvidia/Llama-3.1-8B-Instruct-FP8"
max_tokens: 512
temperature: 0.5
tags:
- openai-compatible
resources:
accelerator: B200
cpu: "1"
memory: 10Gi
use_gpu: true
weights:
- source: "hf://nvidia/Llama-3.1-8B-Instruct-FP8@main"
mount_location: "/app/model_cache/trt_model"
auth_secret_name: "hf_access_token"
- source: "hf://yuhuili/EAGLE3-LLaMA3.1-Instruct-8B@main"
mount_location: "/app/model_cache/eagle3_draft"
auth_secret_name: "hf_access_token"
secrets:
hf_access_token: null
trt_llm:
build:
checkpoint_repository:
repo: michaelfeil/empty-model
revision: main
source: HF
inference_stack: v2
runtime:
enable_chunked_prefill: true
max_batch_size: 512
max_num_tokens: 16384
max_seq_len: 131072
tensor_parallel_size: 1
served_model_name: nvidia/Llama-3.1-8B-Instruct-FP8
patch_kwargs:
model_path: /app/model_cache/trt_model
backend: pytorch
sampler_type: TorchSampler
guided_decoding_backend: xgrammar
max_beam_width: 1
max_input_len: 131072
trust_remote_code: 1
cuda_graph_config:
enable_padding: true
max_batch_size: 512
kv_cache_config:
dtype: fp8
enable_block_reuse: true
free_gpu_memory_fraction: 0.9
speculative_config:
decoding_type: Eagle
max_draft_len: 3
speculative_model_dir: /app/model_cache/eagle3_draft
eagle3_one_model: true
version_overrides:
v2_llm_version: null
runtime:
predict_concurrency: 512
```
This config tells Baseten to compile a TensorRT-LLM engine for Llama 3.1 8B Instruct on a single B200, pulling FP8 weights from `nvidia/Llama-3.1-8B-Instruct-FP8` and an EAGLE3 draft speculator from `yuhuili/EAGLE3-LLaMA3.1-Instruct-8B`. The runtime is tuned for high concurrent throughput: 512 in-flight requests, chunked prefill, an FP8 KV cache, and CUDA graphs sized to the same batch ceiling so the engine stays hot under load.
## 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 | `1` |
| Max sequence length | `131072` |
| Max batch size | `512` |
| Max batched tokens | `16384` |
| Chunked prefill | `enabled` |
| Inference stack | `v2` |
| Served model name | `nvidia/Llama-3.1-8B-Instruct-FP8` |
## Deploy
Push the config to Baseten:
```sh theme={"system"}
uvx truss push
```
You should see output similar to:
```text theme={"system"}
✨ Model llama-3.1-8b-instruct-throughput 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="nvidia/Llama-3.1-8B-Instruct-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": "nvidia/Llama-3.1-8B-Instruct-FP8",
"messages": [
{"role": "user", "content": "What is machine learning?"}
]
}'
```