What GPU do I need to run meta-llama/Llama-4-Scout-17B-16E-Instruct?
A 109B (MoE) vision-language model that reads images alongside text. 108.6B parameters, published in BF16. View on Hugging FaceGated
Llama-4-Scout-17B-16E-Instruct is published by meta-llama on Hugging Face, with 231,450 downloads and 1,337 likes to date. It's a Llama4ForConditionalGeneration model built for image-text-to-text, published natively in BF16, and gated: you'll need to accept the model's terms on Hugging Face before downloading weights.
What Llama-4-Scout-17B-16E-Instruct is
Llama-4-Scout-17B-16E-Instruct is a 109B-parameter mixture-of-experts vision-language model published by Meta on Hugging Face. It is released under Custom license.
License note: a lab-specific license (tagged "other" on Hugging Face); read the model card's own license section before commercial use. Facts in this section are sourced from Llama-4-Scout-17B-16E-Instruct's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Image understanding and captioning
- Visual question answering
- Document/OCR-style reading
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 202.4 GB | 242.8 GB | RTX A6000 | 6 | $2.18/hr |
| FP8 (quantized) | 101.2 GB | 121.4 GB | RTX 5060 Ti | 8 | $0.880/hr |
| INT4 (quantized) | 50.6 GB | 60.7 GB | A100 | 1 | $1.21/hr |
| cheaper alt. | RTX 5060 Ti | 4 | $0.440/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run Llama-4-Scout-17B-16E-Instruct at its published (BF16) precision: 6× RTX A6000, at $0.363/hr per GPU ($2.18/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Llama-4-Scout-17B-16E-Instruct: common questions
Can Llama-4-Scout-17B-16E-Instruct run on a single GPU?
No. At BF16 it needs 242.8 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 48.0 GB RTX A6000, and it takes 6 of them.
Do I need approval to download Llama-4-Scout-17B-16E-Instruct?
Yes. meta-llama gates this repository on Hugging Face, so you have to accept its terms with a signed-in account before the weights will download. It does not change the 242.8 GB the model needs once you have them.
How many GPUs do I need to run Llama-4-Scout-17B-16E-Instruct?
6 at BF16. It needs 242.8 GB of VRAM and the cheapest capable live offer is a 48.0 GB RTX A6000, so 6 of them come to $2.18/hr in total.
Does quantizing Llama-4-Scout-17B-16E-Instruct lower the GPU bill?
Yes. At BF16 the cheapest live fit is 6 RTX A6000 cards at $2.18/hr. At FP8 (quantized) it drops to 8 RTX 5060 Ti cards at $0.880/hr, provided a quantized checkpoint exists for it.
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 podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Llama 4 models
- Llama-4-Maverick-17B-128E-Instruct-FP8 (401.6B, F8_E4M3)
Related reading: RTX A6000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
