Llama 4

Meta’s Llama 4 Scout is a 17B-active MoE with native multimodal support and a 10M token context window.

Tool callingLong contextMultimodal (image)

Meta’s Llama 4 Scout is a 17B-active MoE with native multimodal support and a 10M token context window.

Setup

Install the Baseten CLI and sign in, then install the OpenAI SDK.

Install and sign in to Baseten

macOS or Linux

Terminal

brew tap basetenlabs/baseten
brew install baseten

Windows

Download and extract the binary, then move baseten.exe to a directory on your PATH:

Terminal

Invoke-WebRequest `
  https://github.com/basetenlabs/baseten-cli/releases/download/v0.4.0/baseten_0.4.0_windows_amd64.zip `
  -OutFile baseten.zip; Expand-Archive -Force baseten.zip .

For other platforms or a specific version, see the Baseten CLI install reference.

baseten auth login

Install the OpenAI SDK

uv pip install openai

Prefer not to install? Sign in with uvx truss login --browser and deploy with uvx truss push.

This preset serves Llama 4 Scout on H100:4 with a 128K serving context and native multimodal support.

Hardware

H100 × 4

Engine

vLLM (0.22.0-cu129 build)

Context

128K

Concurrency

256

Write the config

Create and move into the project directory:

mkdir llama-4-scout-latency && cd llama-4-scout-latency

Then create a file named config.yaml and paste the following:

config.yaml

model_name: "model:llama-4-scout preset:latency"
model_metadata:
  description: >-
    Llama 4 Scout 17B multimodal instruct (RedHat FP8-dynamic), long-context with TP=4 FP8 KV,
    OpenAI-compatible chat using vLLM.
  repo_id: RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic
  example_model_input:
    model: llama
    messages:
      - role: user
        content: "Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to target. You may assume that each input would have exactly one solution, and you may not use the same element twice. You can return the answer in any order. class Solution: def twoSum(self, nums: List[int], target: int) -> List[int]:"
    stream: true
    max_tokens: 512
    temperature: 0.5
  tags:
    - openai-compatible
base_image:
  image: vllm/vllm-openai:v0.22.0-cu129
weights:
  - source: "hf://RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic@main"
    mount_location: "/app/checkpoint/model"
    auth_secret_name: "hf_access_token"
secrets:
  hf_access_token: null
environment_variables:
  VLLM_LOGGING_LEVEL: WARNING
  VLLM_ENGINE_READY_TIMEOUT_S: "3600"
docker_server:
  start_command: >-
    sh -c 'GPU_COUNT=$(nvidia-smi --list-gpus | wc -l) && vllm serve /app/checkpoint/model
    --served-model-name llama
    --host 0.0.0.0
    --port 8000
    --trust-remote-code
    --max-model-len 131072
    --tensor-parallel-size $GPU_COUNT
    --distributed-executor-backend mp
    --gpu-memory-utilization 0.95
    --kv-cache-dtype fp8
    --limit-mm-per-prompt.image 10
    --override-generation-config.attn_temperature_tuning true
    --enable-prefix-caching'
  readiness_endpoint: /health
  liveness_endpoint: /health
  predict_endpoint: /v1/chat/completions
  server_port: 8000
resources:
  accelerator: H100:4
  use_gpu: true
runtime:
  predict_concurrency: 256
  health_checks:
    restart_check_delay_seconds: 1800
    restart_threshold_seconds: 1200
    stop_traffic_threshold_seconds: 120

Flags

The start_command passes these flags to the engine. Each one controls a runtime or serving behavior:

Flag	Value	What it does
--trust-remote-code	(no value)	Execute model-specific Python from the checkpoint (required for many Qwen, Phi, and custom architectures).
--max-model-len	131072	Maximum context length (tokens) the server accepts per request.
--tensor-parallel-size	$GPU_COUNT	Number of GPUs to shard the model across.
--distributed-executor-backend	mp	How vLLM coordinates tensor-parallel workers across processes. mp: Python multiprocessing (single-node default).
--gpu-memory-utilization	0.95	Fraction of GPU memory vLLM may use for weights and KV cache.
--kv-cache-dtype	fp8	KV cache numeric precision. fp8: ~2× KV cache density with negligible quality impact on most models.
--limit-mm-per-prompt.image	10	Maximum number of image inputs per prompt.
--override-generation-config.attn_temperature_tuning	true	Sets the attn_temperature_tuning field in the model’s generation config.
--enable-prefix-caching	(no value)	Reuse KV cache across requests that share a prefix.

Deploy

Push the config to Baseten with the Baseten CLI, or with the Truss CLI if you prefer it:

Baseten CLI

baseten model push

You should see output similar to:

Pushing model "llama-4-scout-latency"...
Uploading model...
Uploaded model in 0s
✨ Model llama-4-scout-latency was successfully pushed ✨

  Model:       llama-4-scout-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

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="llama",
    messages=[
        {"role": "user", "content": "What is machine learning?"}
    ],
)

print(response.choices[0].message.content)

cURL

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": "llama",
    "messages": [
      {"role": "user", "content": "What is machine learning?"}
    ]
  }'

Next steps

Call your model

Endpoint anatomy, authentication, and sync versus async inference

Autoscaling

Scale replicas with traffic, including scale to zero