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 times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. 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 | AMD MI300X | 1 | $2.63/hr |
| cheaper alt. | RTX 4000 SFF Ada | 7 | $1.39/hr | ||
| INT4 (quantized) | 50.6 GB | 60.7 GB | A100 | 1 | $1.16/hr |
| cheaper alt. | RTX A5000 | 3 | $0.528/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: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
KV-cache size for Llama-4-Scout-17B-16E-Instruct is not published for this architecture, so no figure is shown.
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 INT4 (quantized) it drops to one A100 at $1.16/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)
Alternatives at this size
Other models for image-text-to-text within about a third of Llama-4-Scout-17B-16E-Instruct's 108.6B parameters, from other model lines.
- Qwen3.5-122B-A10B (125.1B, BF16)
- GLM-4.5V (107.7B, BF16)
- InternVL3-78B (78.4B, BF16)
More on Llama-4-Scout-17B-16E-Instruct
Fits on a 288 GB GPU at BF16: every model that fits in 288 GB.
Fits on a 141 GB GPU at FP8: every model that fits in 141 GB.
Fits on an 80 GB GPU at INT4: every model that fits in 80 GB.
Best vision-language models: how Llama-4-Scout-17B-16E-Instruct ranks against the rest.
Related reading: RTX A6000 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
