Deploy LLaMA 4
Text & ChatLLaMA 4 is Meta's latest open-weight model family. Scout uses a 109B MoE architecture with 17B active parameters, 10M token context window, and native multimodal capabilities. LLaMA 3.3 70B remains a strong general-purpose option.
Deploy LLaMA 4 in minutes
Starting at $0.66/hr on dedicated GPU
Available Variants (2)
| Model | GPU | VRAM | Price | Action |
|---|---|---|---|---|
LLaMA 4 Scout Scout (109B MoE) | A100 80GB PCIe | 80 GB | $1.85/hr | Deploy |
LLaMA 3.3 70B Large (70B) | RTX A6000 | 48 GB | $0.66/hr | Deploy |
Prices include the service fee. Charges follow actual running time.
Requirements
ModelPilot assigns 48–80GB cloud GPUs across the listed variants. Actual local VRAM requirements vary with model variant, precision, quantization, resolution, and workflow settings.
On ModelPilot, deploy on a dedicated cloud GPU (up to 80GB VRAM) starting at $0.66/hr with no setup required.
Use Cases
- ✓General-purpose AI assistants
- ✓Long-context document processing
- ✓Multimodal understanding
- ✓Enterprise AI applications
Related Models
Frequently Asked Questions
How much GPU memory is allocated for LLaMA 4?
The listed ModelPilot variants use 48–80GB cloud GPUs. Local memory needs vary with the variant, precision, quantization, and workflow settings.
How much does it cost to run LLaMA 4?
Starting at $0.66/hr on a dedicated GPU. Charges are calculated from actual running time, with auto-stop when credits run out.
How long does LLaMA 4 take to deploy?
Text models typically deploy in 5–15 minutes including model download.
Can I run LLaMA 4 on my local GPU?
It depends on the selected variant, precision, quantization, and workflow settings. Compare the variants below with your available VRAM; the table shows ModelPilot's cloud GPU allocation, not a universal local minimum.
Ready to deploy LLaMA 4?
Pick your GPU and have it running in minutes. No infrastructure setup required.