AIARCOASC Docs

Parallel File System

PB-scale POSIX file system mountable on GPU pods. Throughput tiers from 1 GB/s to 1 TB/s.

What it is

When object storage isn't enough — when your training script does random reads, mmap, or hits the file system harder than the warm-pool loader can mask — mount a parallel file system into your pod. POSIX semantics, no application changes.

When to use it

  • Training datasets too large for local NVMe but too random-access for object storage.
  • Shared scratch space across nodes in a multi-node job.
  • Long-running notebook environments with heavy filesystem use.

Quickstart

asc pfs create scratch --region apac --capacity 100Ti --throughput 50GBs
asc pods run --gpu h100 --image my/img --mount-pfs scratch:/scratch
fs = client.pfs.create(name='scratch', region='apac', capacity_tib=100, throughput_gbs=50)
client.pods.run(image='my/img', gpu='h100', mounts=[{'pfs': fs.id, 'path': '/scratch'}])
const fs = await client.pfs.create({ name: 'scratch', region: 'apac', capacityTib: 100, throughputGbs: 50 });
await client.pods.run({ image: 'my/img', gpu: 'h100', mounts: [{ pfs: fs.id, path: '/scratch' }] });

Limits & quotas

LimitDefaultBurstNotes
Min capacity10 TiB
Max capacity10 PiBLift via sales
Throughput tiers1 / 10 / 50 / 200 / 1000 GB/s

Pricing

See pricing. Pay-as-you-go, billed monthly via Stripe.

API surface

  • POST /v1/pfs — create
  • GET /v1/pfs — list
  • DELETE /v1/pfs/{id} — delete

Required scope(s): storage:write. See Scopes.

Security

All access is authenticated and scoped. See Auth & scopes and Network controls.