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Llama 4 Scout 17B 16E icon

Llama 4 Scout 17B 16E

NVIDIA
Llama-4-Scout-17B-16E is a natively multimodal mixture-of-experts (MoE) large language model built for advanced text and image understanding. It activates 17B parameters (109B total) across 16 experts and is built on a 48-layer transformer architecture with 40 attention heads and a 5,120 hidden size. The model supports multilingual text and image inputs and generates multilingual text and code outputs. With a 10M token context window, it is optimized for long-context reasoning, large-scale document analysis, and production-ready multimodal AI deployments.
TypeVision-Language Model
CapabilitiesText Generation, Instruction Following, Reasoning, Mathematical Reasoning+6 more
Group Release DateApril 4, 2025
Links
Blog|HF Model Card
LicenseLlama4

Inference Instructions

Deploy and run this model on NVIDIA B200 GPUs using the command below. Copy the command to get started with inference.

CONSOLE
docker run --gpus all 
 --shm-size 128g 
 -p 8000:8000 
 -v ~/.cache/huggingface:/root/.cache/huggingface 
 -e HF_TOKEN='YOUR_HF_TOKEN' 
 --ipc=host 
 lmsysorg/sglang:v0.5.8-cu130 
 python3 -m sglang.launch_server 
 --model-path meta-llama/Llama-4-Scout-17B-16E 
 --host 0.0.0.0 
 --port 8000 
 --max-prefill-tokens 65536 
 --max-running-requests 1024 
 --tp 8 
 --mem-fraction-static 0.95 
 --trust-remote-code

Model Benchmarks

Each model was tested with a fixed input size and total token volume while increasing concurrency to measure serving performance under load.

ITL vs Concurrency

Time to First Token

Throughput Scaling

Total Tokens/sec vs Avg TTFT

Vultr Cloud GPU

NVIDIA HGX B200

Deploy NVIDIA B200 on Vultr Cloud GPU

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