Quick selection by workload
Start by identifying your primary workload type:| Workload | Recommended GPU tier | Minimum VRAM | Notes |
|---|---|---|---|
| LLM inference (7B–13B params) | Mid-range (RTX 4090, L4) | 24 GB | Sufficient for most quantized models |
| LLM inference (30B–70B params) | High-end (A100, H100) | 48–80 GB | May require multi-GPU setup |
| LLM training/fine-tuning | High-end (A100, H100) | 40–80 GB | Memory bandwidth critical |
| Image generation (SDXL, Flux) | Mid-range (RTX 4090, L4) | 16–24 GB | Benefits from fast inference |
| Computer vision | Entry to mid-range | 8–16 GB | Depends on model and batch size |
| 3D rendering | Mid-range with RT cores | 16–24 GB | RT cores accelerate ray tracing |
| Data processing | CPU-focused or entry GPU | 8 GB+ | Prioritize CPU cores and RAM |
Estimate VRAM requirements
VRAM is the most common bottleneck. Use these guidelines: For LLMs: Allocate approximately 2 GB of VRAM per billion parameters. For example:- 7B model → ~14 GB VRAM
- 13B model → ~26 GB VRAM
- 70B model → ~140 GB VRAM (requires multi-GPU)
Resource calculators
Use these tools to estimate your specific requirements:- Hugging Face Model Memory Calculator: Memory estimates for transformer models
- Can it run LLM?: Check if hardware can run specific language models
- VRAM Estimator: GPU memory requirement approximations
Storage configuration
Choose storage based on your data persistence needs:| Storage type | Persists after stop? | Persists after delete? | Best for |
|---|---|---|---|
| Container disk | No | No | OS, temporary files |
| Volume disk | Yes | No | Working files, checkpoints |
| Network volume | Yes | Yes | Datasets, model weights, long-term storage |
Optimize for cost
- Right-size your resources: Start with the minimum viable configuration, then scale up based on actual usage. Development and testing often need less power than production.
- Use spot instances: For fault-tolerant workloads like batch processing or training with checkpoints, spot instances offer significant savings.
- Consider savings plans: For extended usage, Runpod’s savings plans reduce costs for committed usage.
Secure Cloud vs Community Cloud
| Secure Cloud | Community Cloud | |
|---|---|---|
| Infrastructure | T3/T4 data centers | Peer-to-peer providers |
| Reliability | High redundancy | Variable |
| Best for | Production, sensitive data | Cost-sensitive workloads |
| Pricing | Standard | Competitive |
Runpod is no longer accepting new hosts for Community Cloud. Existing Community Cloud resources remain available.
Next steps
Deploy a Pod
Create your first Pod with your chosen configuration.
GPU types reference
Compare all available GPUs and specifications.
Storage options
Learn more about storage types and pricing.
Manage Pods
Learn how to create, start, stop, and delete Pods.