Object store built for AI workloads

Object store built for AI workloads

High performance, AI-native objectstore with S3 API included

16 followers

Visit website
Anvil is an open-source, AI-native object store designed for modern workloads. We built it after hitting the limits of Git LFS, Hugging Face repos, S3, and others when working with multi-GB model files. It is S3-compatible & gRPC-native, supports: * Model-aware indexing - so it understands safetensors, gguf, and ONNX. * Tensor-level streaming * Erasure-coded storage * Open source (Apache-2.0) If you’re storing large models, versioning fine-tunes, running local inference, we want your feedback.
Object store built for AI workloads gallery image
Free
Launch tags:Storage•Software Engineering•Artificial Intelligence
Launch Team
Auth0
Auth0
Start building with Auth0 for AI Agents, now generally available.
Promoted

What do you think? …

Courtney Robinson
Maker
📌
Hey all, We’re the team behind Anvil. We built this because every existing option failed when serving large models: Git LFS broke on multi-GB safetensors HF repos weren’t ideal for private/internal hosting S3/MinIO treated model files as “dumb blobs” Full-model downloads were too slow for inference Replication made storage 3× the cost Fine-tunes duplicated 10–20GB base models repeatedly So we built Anvil as the object store we wish existed. It’s S3-compatible, self-hosted, open-source, and understands ML model formats natively. We run it in production on an 18-node cluster and are finally releasing it to the world. Happy to answer every question — technical deep dives welcome! If you like what we built, an upvote means the world to us ❤️
Olaoluwa Mercy Deborah

@zcourts this will really help cut cost tbh