Choose a Pod

Select the right Pod by evaluating your resource requirements.

Selecting the right Pod configuration maximizes performance and cost efficiency for your workload. This guide helps you match your requirements to the right GPU, VRAM, and storage configuration.

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

For a full list of available GPUs and their specifications, see GPU types.

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)

Quantization reduces VRAM requirements significantly. A 4-bit quantized 70B model can run on ~35 GB VRAM.

For image models: SDXL requires ~8 GB minimum, but 16–24 GB provides headroom for larger batch sizes and LoRA training.

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

For data-intensive workloads, ensure sufficient volume disk or network volume capacity for your datasets, model weights, and output files.

Optimize for cost

1. 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.

2. 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.