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AI-Powered Hyperscale Object Storage

PutFS accelerates AI/ML workloads by leveraging its distributed architecture and object storage capabilities. During model training, PutFS's distributed setup allows parallel data access and I/O operations, reducing latency and speeding up training times. For model inference, PutFS's high-throughput data access ensures rapid retrieval and deployment of data stored for AI models, enabling predictions with minimal latency. Crucially, PutFS scales linearly from 100 TB to 100 PB and beyond.

Lol, kidding. None of that means anything.

What's actually going on

Every object storage startup now claims to be "AI-native" or "the storage layer for the AI revolution." The pitch is always the same: buzzword soup about parallel I/O, linear scaling to exabytes, and being "the core of the AI ecosystem." It's marketing for VCs, not engineering for users.

Here's a very comprehensive feature list of what AI/ML workloads actually need from storage:

  • read files (fast)
  • write files (fast)
  • list directories (fast)

That's it. And that's what every workload needs from storage.

So if you're looking for "AI-native storage," you're looking for a filesystem with good caching and fast I/O.

Aren't we all? :)