Object store built for AI workloads

What it does

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.

Introducing Anvil — the AI-Native Object Store

Fast, self-hosted, S3-compatible storage designed for models, safetensors, gguf files, ONNX artifacts, and large ML datasets. GitHub: https://github.com/worka-ai/anvil Latest Release: https://github.com/worka-ai/anvil/releases/latest Docs: https://worka.ai/docs/anvil/getting-started Landing Page: https://worka.ai/anvil We didn’t set out to build a new object store. We set out to build our app — and everything broke in predictable, painful ways. There was no storage layer designed for AI workloads — only general-purpose object stores that weren’t aware of model formats or inference patterns. Anvil is a self-hosted, S3-compatible, open-source object store designed for AI-native workloads. It…from worka.ai

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