Network Volume for Persistent Storage | Generated by AI

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Question: RunPod storage: choose Volume Disk or Network Volume?

Answer:

For most serious workloads (models, datasets, deployments), choose Network Volume.

Comparison:

	Volume Disk	Network Volume
Scope	Single Pod only	Shared across Pods
Lifetime	Tied to Pod lifecycle	Independent of Pods
Delete Pod	Data deleted	Data remains
Move to another GPU	No	Yes
Multiple Pods	No	Yes
Startup speed	Fast (local disk)	Slightly slower
Cost	$0.10/GB/mo running, $0.20/GB/mo stopped	Usually cheaper for long-lived storage
Best for	Temporary cache	Models, datasets, production

For your use case:

• SGLang
• LibreChat
• LLM weights (7B/14B/30B)
• model cache
• training data
• scripts you want to reuse

→ Network Volume is the right choice.

Typical setup:

RunPod Network Volume
        |
        mounted at
        |
    /workspace

        |
        +-- models/
        |     +-- llama/
        |     +-- qwen/
        |
        +-- datasets/
        |
        +-- scripts/
        |
        +-- configs/

Then you can:

1. Create Pod A with RTX 4090
2. Download models:

cd /workspace
huggingface-cli download Qwen/Qwen3-8B --local-dir models/qwen3-8b

1. Delete Pod A.

Later:

1. Create Pod B with A6000/H100/RTX 2000 Ada
2. Attach the same Network Volume
3. Models are already there.

For AI development, the pattern is:

Network Volume = persistent state
Pod = disposable compute

Similar to:

Docker container  -> Pod
Docker volume    -> Network Volume

I would avoid putting anything important in /workspace on the normal Volume Disk.

For your current RunPod setup (SGLang + LibreChat chatbot), I would use:

Network Volume: 50-200GB
    /workspace/models
    /workspace/huggingface-cache
    /workspace/projects

Then recreate Pods freely when changing GPUs.

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