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Shimmy

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⚡ Python-free Rust inference server — OpenAI-API compatible. GGUF + SafeTensors, hot model swap, auto-discovery, single binary. FREE now, FREE forever.

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OSS

Shimmy

Added 1 June 2026

#api-server #command-line-tool #developer-tools #gguf #huggingface #huggingface-models #huggingface-transformers #inference-server

Overview

Shimmy is a Rust-based inference server that is compatible with the OpenAI API. It supports GGUF and SafeTensors formats, offers hot model swapping and auto-discovery, and runs as a single binary with no Python dependency. The tool is free and open source.

Best for

Best for
Developers seeking a free, no-fuss Rust-based inference server with OpenAI API compatibility

Use cases

  • Serve language model inferences with an OpenAI-compatible API
  • Swap models without restarting the server during development or testing
  • Deploy a lightweight, self-contained inference endpoint in a Rust environment

Notes

Shimmy is a Rust-based inference server that is compatible with the OpenAI API. It supports GGUF and SafeTensors formats, offers hot model swapping and auto-discovery, and runs as a single binary with no Python dependency. The tool is free and open source.

5,306 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

  • Serve language model inferences with an OpenAI-compatible API
  • Swap models without restarting the server during development or testing
  • Deploy a lightweight, self-contained inference endpoint in a Rust environment

Pros

  • Single binary with no Python runtime required
  • Free and open source with a permissive license
  • Supports hot model swapping for flexible experimentation

Cons

  • Smaller community and fewer integrations compared to more established inference servers
  • Limited to GGUF and SafeTensors model formats
  • May lack advanced monitoring or logging features found in dedicated observability tools

Indexed from awesome-llmops and enriched against its public facts.

Pros

  • Single binary with no Python runtime required
  • Free and open source with a permissive license
  • Supports hot model swapping for flexible experimentation

Cons

  • Smaller community and fewer integrations compared to more established inference servers
  • Limited to GGUF and SafeTensors model formats
  • May lack advanced monitoring or logging features found in dedicated observability tools
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