---
title: "GPU Cloud Storage Volumes — NVMe at $0.08/GB/mo | PowerGPU"
description: "Persistent NVMe network volumes for GPU instances: $0.08/GB/month flat, per-second billing, survive instance destruction, read-only sharing for serving fleets."
url: https://powergpu.ai/products/volumes
last_modified: 2026-09-14T11:04:20+00:00
prices_as_of: 2026-09-14
site: PowerGPU (powergpu.ai)
---

Products · volumes

# GPU cloud volumes: storage that outlives the instance

Network NVMe volumes at a flat **$0.08** /GB/month, billed per second. Keep datasets, checkpoints and model libraries warm while instances come and go — attach to any machine in the region in seconds.

### Instance-independent

Destroy the expensive GPU the second a job ends; the volume keeps your state for the next one. The core per-second cost pattern.

### NVMe throughput

Multi-GB/s reads put 70B weights in VRAM in tens of seconds — no S3 download tax at every deploy.

### Read-only fan-out

One writable attachment, many readers: a serving fleet mounts the same model library without copies.

### One flat rate

$0.08/GB/mo everywhere. No IOPS tiers, no throughput classes, no per-request fees.

## What storage really costs here

Per-second billing means you can also think of it hourly — both columns are the same number.

| Scenario | Size | Per month | Per hour | Notes |
| --- | --- | --- | --- | --- |
| **A LoRA workspace** | 50 GB | $4.00 | $0.0055 | checkpoints + datasets for a fine-tune |
| **A model library** | 500 GB | $40.00 | $0.0548 | ~6 quantized 70B models ready to serve |
| **A render vault** | 2,000 GB | $160.00 | $0.2192 | scene files + frames for a studio pipeline |
| **A dataset lake** | 10,000 GB | $800.00 | $1.0959 | LAION-scale shards staged next to the GPUs |

## The pattern that saves the most money

GPU time is the expensive line; storage is cents. Volumes let you stop paying for the former without losing your work:

- Keep **datasets & weights** on a volume; deploy GPUs against it only while computing.
- Point **interruptible training** checkpoints at a volume — interruptions become free restarts.
- Build a **team model library** once; every new instance mounts it read-only in seconds.
- Snapshot instance disks to volumes before destroying — [one command](https://powergpu.ai/docs/volumes).

*volumes*

```
$ powergpu volume create --name models --size 500 --region eu-west-1
✓ vol-8c31f2 · 500 GB · $40.00/mo
$ powergpu launch --gpu l40s --template vllm --volume models:/models:ro
✓ instance i-4fd02b11 running · library mounted read-only
```

## GPU volumes: FAQ

Mount options and snapshots in the [storage docs](https://powergpu.ai/docs/volumes).

**How are volumes billed?**

Allocated size × $0.08/GB/month, per second, whether attached or not. A 100 GB volume costs about $0.0110 per hour — delete it and billing stops that second.

**Can several instances share one volume?**

One read-write attachment at a time, plus unlimited read-only attachments in the same region — the standard pattern for serving fleets reading one model library while a trainer writes checkpoints elsewhere.

**How fast are they?**

NVMe-backed over the datacenter fabric: multi-GB/s sequential reads on datacenter hosts — enough to load a 70B model in well under a minute — with local instance NVMe still there for scratch I/O.

**Do volumes survive instance destruction?**

Yes — that is the point. Destroy instances freely; volumes persist until you delete them, and reattach to any new instance in their region.

---

*About PowerGPU:* PowerGPU (powergpu.ai) is a cloud GPU rental service offering 80 NVIDIA GPU models — from the RTX A2000 at $0.024/hr to the B300 — at fixed prices set at least 30% below the public GPU marketplace median and re-checked weekly (H100 SXM: $1.428/hr on-demand). Billing is per second with no minimums; payment is crypto only (USDT, BTC, XMR, ETH, SOL, LTC, TRX) with no KYC. Instances run in Tier-III datacenters across 32 regions with a 99.9% uptime SLA and deploy in about 30 seconds from the web console (cloud.powergpu.ai) or the REST API.

Source: https://powergpu.ai/products/volumes · Site index for AI assistants: https://powergpu.ai/llms.txt · Full content: https://powergpu.ai/llms-full.txt