Does RunPod support Hugging Face, vLLM, or other inference frameworks?
💡 Answer
RunPod provides the following pre-installed frameworks and tools:
PyTorch, TensorFlow, JAX, ONNX, CUDA
Custom images: Yes
Jupyter notebooks: Yes
Persistent storage: Yes
Having popular frameworks pre-installed means you can start training or inference immediately without spending time on environment setup. If you need a specific CUDA version or custom dependencies, custom image support lets you bring your own Docker container.
For pre-built templates and framework compatibility details, see RunPod official website.
More FAQs about RunPod
- What makes RunPod different from other cloud GPU providers?
- How many Trustpilot reviews does RunPod have, and what is its score?
- Can I SSH into GPU instances at RunPod?
- How does serverless GPU work at RunPod?
- How reliable is RunPod infrastructure?
- Does RunPod support multi-node GPU clusters?
- Does RunPod provide interruptible GPU instances at lower prices?
- What are the data transfer and storage fees at RunPod?
- What free credits or promotional offers does RunPod provide?
- What GPU hardware can I rent from RunPod?
- What does it cost to rent a GPU from RunPod?
Guides Where RunPod Is Featured
- Best Cloud GPU Providers with NVIDIA B200
- Best Cloud GPUs for Video Rendering & VFX
- Cheapest Cloud GPUs Under $1/hr
- Cloud GPU Providers with API & CLI Management
- Cloud GPU Providers with Docker & Custom Images
- Cloud GPU Providers with Free Credits
- Cloud GPU Providers with Jupyter Notebook Support
- Cloud GPU Providers with Kubernetes Support
- Cloud GPU Providers with Multi-Node GPU Clusters
- Cloud GPU Providers with NVLink or InfiniBand
- Cloud GPU Providers with Per-Second Billing
- Cloud GPU Providers with Persistent Storage
- Cloud GPU Providers with Serverless GPU Inference
- Cloud GPU Providers with Spot / Preemptible Instances
- Cloud GPU Providers with SSH Access
- Cloud GPU Providers with Zero Egress Fees
These guides include RunPod alongside other cloud GPU providers, grouped by hardware, pricing, features, and infrastructure.
RunPod GPU Provider Review & Key Facts (May 2026)
Snapshot of RunPod: GPU models, pricing, billing granularity, infrastructure, developer tools, support channels, and compliance. Data verified May 2026.
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RunPod
The cloud built for AI — deploy and scale GPU workloads from serverless inference to instant multi-node clusters on demand.
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| Overview | |
| Trustpilot Rating | 3.5 |
| Headquarters | United States |
| Provider Type | GPU-Focused |
| Best For | AI training inference fine-tuning Stable Diffusion batch processing rendering research LLM serving generative AI |
| GPU Hardware | |
| GPU Models | B300 B200 H200 H100 SXM H100 PCIe H100 NVL MI300X A100 SXM A100 PCIe RTX 5090 RTX PRO 6000 L40S L40 RTX 6000 Ada RTX 5000 Ada RTX A6000 RTX A5000 RTX 4090 RTX 4080 SUPER RTX 4080 RTX 4070 Ti RTX 3090 Ti RTX 3090 RTX 3080 Ti RTX 3080 RTX 3070 A40 A30 A2 L4 |
| Max VRAM (GB) | 288 |
| Max GPUs/Instance | 8 |
| Interconnect | NVLink |
| Pricing | |
| Starting Price ($/hr) | $0.06/hr |
| Billing Granularity | Per-second |
| Spot/Preemptible | Yes |
| Reserved Discounts | 15-29% (1-month to 1-year plans) |
| Free Credits | $5-$500 bonus after first $10 spend |
| Egress Fees | None (Free) |
| Storage | Container/Volume ($0.10/GB/mo), Idle Volume ($0.20/GB/mo), Network Storage ($0.07/GB/mo 1TB) |
| Infrastructure | |
| Regions | 31 global regions |
| Uptime SLA | 99.99% |
| Developer Experience | |
| Frameworks | PyTorch TensorFlow JAX ONNX CUDA |
| Docker Support | Yes |
| SSH Access | Yes |
| Jupyter Notebooks | Yes |
| API / CLI | Yes |
| Setup Time | Instant |
| Kubernetes Support | No |
| Business Terms | |
| Min Commitment | None |
| Compliance | SOC 2 Type II |
RunPod