Vision
How to deploy Llama-4-Scout-17B-16E-Instruct on a GPU cloud
A 109B (MoE) vision-language model that reads images alongside text. Full specs, license and use cases.
Llama-4-Scout-17B-16E-Instruct size and hardware requirements
108.6B
Total parameters
~17B active per token across 16 experts (mixture-of-experts)
Active parameters
BF16
Published precision
242.8 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 202.4 GB | 242.8 GB | RTX A6000 | 6 | $1.98/hr |
| FP8 (quantized) | 101.2 GB | 121.4 GB | RTX 4000 SFF Ada | 7 | $1.26/hr |
| INT4 (quantized) | 50.6 GB | 60.7 GB | A100 | 1 | $0.851/hr |
How to run Llama-4-Scout-17B-16E-Instruct
Run Llama-4-Scout-17B-16E-Instruct with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve meta-llama/Llama-4-Scout-17B-16E-Instruct --tensor-parallel-size 6Run Llama-4-Scout-17B-16E-Instruct with GGUF quantizations
Prebuilt GGUF weights published at unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/Llama-4-Scout-17B-16E-Instruct-GGUFSource: https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
Deploy Llama-4-Scout-17B-16E-Instruct on Aquanode
Aquanode has no one-click deploy template for Llama-4-Scout-17B-16E-Instruct; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (6× RTX A6000 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod