Vision
How to deploy Llama-4-Maverick-17B-128E-Instruct-FP8 on a GPU cloud
A 402B (MoE) vision-language model that reads images alongside text. Full specs, license and use cases.
Llama-4-Maverick-17B-128E-Instruct-FP8 size and hardware requirements
401.6B
Total parameters
~17B active per token across 128 experts (mixture-of-experts)
Active parameters
F8_E4M3
Published precision
448.9 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 374.1 GB | 448.9 GB | RTX PRO 6000 | 5 | $8.20/hr |
| INT4 (quantized) | 187.0 GB | 224.4 GB | RTX A6000 | 5 | $1.65/hr |
How to run Llama-4-Maverick-17B-128E-Instruct-FP8
Run Llama-4-Maverick-17B-128E-Instruct-FP8 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-Maverick-17B-128E-Instruct-FP8 --tensor-parallel-size 5Deploy Llama-4-Maverick-17B-128E-Instruct-FP8 on Aquanode
Aquanode has no one-click deploy template for Llama-4-Maverick-17B-128E-Instruct-FP8; 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 (5× RTX PRO 6000 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