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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 (0.22.0-cu129 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
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 via 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
    --load-format runai_streamer"
  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

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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
--trust-remote-code(no value)Execute model-specific Python from the checkpoint (required for many Qwen, Phi, and custom architectures).
--max-model-len131072Maximum context length (tokens) the server accepts per request.
--tensor-parallel-size$GPU_COUNTNumber 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.image10Maximum number of image inputs per prompt.
--override-generation-config.attn_temperature_tuningtrueSets 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.
--load-formatrunai_streamerWeight loading backend. runai_streamer: Stream weights from object storage without materializing to disk.

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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 ✨

   Model ID:      abc1d2ef
   Deployment ID: xyz123
   Endpoint:      model-abc1d2ef.api.baseten.co
   Logs:          https://app.baseten.co/models/abc1d2ef/logs/xyz123
Your model ID is printed in the truss push output (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)