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Tool callingLong contextMultimodal (image)

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Setup

To get started, sign into Baseten with Truss and then install the OpenAI SDK.
Sign in to Baseten
uvx truss login --browser
Install the OpenAI SDK
uv pip install openai
meta-llama/Llama-4-Scout-17B-16E-Instruct is a 109B-parameter MoE model (17B active per token) with up to 10M context. This preset serves Llama 4 Scout on H100:4 with a 128K serving context and native multimodal support.

Hardware

H100 × 4

Engine

vLLM (latest build)

Context

128K

Concurrency

256

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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
base_image:
  image: vllm/vllm-openai:latest
build_commands:
  - pip install hf-xet
model_metadata:
  repo_id: meta-llama/Llama-4-Scout-17B-16E-Instruct
  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
docker_server:
  start_command: >-
    sh -c "HF_TOKEN=$(cat /secrets/hf_access_token) vllm serve meta-llama/Llama-4-Scout-17B-16E-Instruct
    --served-model-name llama
    --max-model-len 131072
    --tensor-parallel-size 4
    --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}'
    --host 0.0.0.0
    --port 8000"
  readiness_endpoint: /health
  liveness_endpoint: /health
  predict_endpoint: /v1/chat/completions
  server_port: 8000
environment_variables:
  hf_access_token: null
resources:
  accelerator: H100:4
  use_gpu: true
secrets:
  hf_access_token: null
runtime:
  predict_concurrency: 256
model_name: "model:llama-4-scout preset:latency"

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Flags

The start_command passes these flags to the engine. Each one controls a runtime or serving behavior:
FlagValueWhat it does
--max-model-len131072Maximum context length (tokens) the server accepts per request.
--tensor-parallel-size4Number of GPUs to shard the model across.
--distributed-executor-backendmpHow vLLM coordinates tensor-parallel workers across processes. mp: Python multiprocessing (single-node default).
--gpu-memory-utilization0.95Fraction of GPU memory vLLM may use for weights and KV cache.
--kv-cache-dtypefp8KV cache numeric precision. fp8: ~2× KV cache density with negligible quality impact on most models.
--limit-mm-per-prompt{"image": 10}Max multimodal inputs accepted per prompt (JSON object keyed by modality).
--override-generation-config{"attn_temperature_tuning": true}JSON overrides applied on top of the model’s default generation config.

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Deploy

Push the config to Baseten: 
uvx truss push
You should see output similar to: 
✨ Model llama-4-scout-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.

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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:
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)