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Flash workers have access to two types of storage: for temporary data and for persistent, sharable data.

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Container disk

A container disk provides temporary storage that exists only while a worker is running. Each worker gets its own isolated container disk, with a default size of 64GB for GPU endpoints. You can read and write temporary files to the container disk using standard filesystem operations from within @Endpoint functions. Any file that is not written to a network volume (at /runpod-volume/) is written to the container disk, and will be erased when the worker stops.

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Configuring container disk size (GPU-only)

Configure container disk size for GPU endpoints using the template parameter (default: 64GB). 
from runpod_flash import Endpoint, GpuType, PodTemplate

@Endpoint(
    name="large-temp-storage",
    gpu=GpuType.NVIDIA_A100_80GB_PCIe,
    template=PodTemplate(containerDiskInGb=100)
)
async def process(data: dict) -> dict:
    # 100GB container disk available
    ...

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CPU auto-sizing

CPU endpoints automatically adjust container disk size based on instance limits:
  • CPU3G and CPU3C instances: vCPU count × 10GB (e.g., 2 vCPU = 20GB)
  • CPU5C instances: vCPU count × 15GB (e.g., 4 vCPU = 60GB)
If you specify a custom size that exceeds the instance limit, deployment will fail with a validation error.

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Network volumes

Network volumes provide persistent storage that survives worker restarts. Each volume is tied to a specific datacenter. Use volumes to share data between endpoint functions or to persist data between runs.

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Attaching network volumes

Attach a network volume using the volume parameter. Flash uses the volume name to find an existing volume or create a new one. Specify the datacenter parameter to control where the volume is created: 
from runpod_flash import Endpoint, GpuType, DataCenter, NetworkVolume

vol = NetworkVolume(name="model-cache", size=100, datacenter=DataCenter.US_GA_2)

@Endpoint(
    name="persistent-storage",
    gpu=GpuType.NVIDIA_A100_80GB_PCIe,
    datacenter=DataCenter.US_GA_2,
    volume=vol
)
async def process(data: dict) -> dict:
    # Access files at /runpod-volume/
    ...
You can also reference an existing volume by ID: 
vol = NetworkVolume(id="vol_abc123")

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Multi-datacenter volumes

For endpoints deployed across multiple datacenters, pass a list of volumes (one per datacenter): 
from runpod_flash import Endpoint, GpuType, DataCenter, NetworkVolume

volumes = [
    NetworkVolume(name="models-us", size=100, datacenter=DataCenter.US_GA_2),
    NetworkVolume(name="models-eu", size=100, datacenter=DataCenter.EU_RO_1),
]

@Endpoint(
    name="global-inference",
    gpu=GpuType.NVIDIA_A100_80GB_PCIe,
    datacenter=[DataCenter.US_GA_2, DataCenter.EU_RO_1],
    volume=volumes
)
async def process(data: dict) -> dict:
    # Workers in each region access their local volume at /runpod-volume/
    ...
Only one network volume is allowed per datacenter. If you specify multiple volumes in the same datacenter, deployment will fail.

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Accessing network volume files

Network volumes mount at /runpod-volume/ and can be accessed like a regular filesystem: 
from runpod_flash import Endpoint, GpuType, NetworkVolume

vol = NetworkVolume(name="model-storage")

@Endpoint(
    name="model-server",
    gpu=GpuType.NVIDIA_A100_80GB_PCIe,
    volume=vol,
    dependencies=["torch", "transformers"]
)
async def run_inference(prompt: str) -> dict:
    from transformers import AutoModelForCausalLM, AutoTokenizer

    # Load model from network volume
    # Persists across worker restarts and shared between workers
    model_path = "/runpod-volume/models/llama-7b"
    model = AutoModelForCausalLM.from_pretrained(model_path)
    tokenizer = AutoTokenizer.from_pretrained(model_path)

    # Run inference
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    outputs = model.generate(**inputs, max_length=100)
    text = tokenizer.decode(outputs[0])

    return {"generated_text": text}

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Load-balanced endpoints with storage

from runpod_flash import Endpoint, GpuType, NetworkVolume

vol = NetworkVolume(name="model-storage")

api = Endpoint(
    name="inference-api",
    gpu=GpuType.NVIDIA_GEFORCE_RTX_4090,
    volume=vol,
    workers=(1, 5)
)

@api.post("/generate")
async def generate(prompt: str) -> dict:
    from transformers import AutoModelForCausalLM

    model = AutoModelForCausalLM.from_pretrained("/runpod-volume/models/gpt2")
    # Generate text
    return {"text": "generated"}

@api.get("/models")
async def list_models() -> dict:
    import os
    models = os.listdir("/runpod-volume/models")
    return {"models": models}

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Creating and managing network volumes

Network volumes must be created before attaching them to an Endpoint. See Network volumes for detailed instructions.