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shimmy

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⚡ Pure-Rust WebGPU inference engine — OpenAI-API compatible, GGUF native, runs on any GPU. No Python. No llama.cpp. Single binary.

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⚡ Pure-Rust WebGPU inference engine — OpenAI-API compatible, GGUF native, runs on any GPU. No Python. No llama.cpp. Single binary.

Shimmy is independently maintained and free forever. Sponsorship funds certification, compatibility work, and releases.

Shimmy will be free forever. No asterisks. No "free for now." No pivot to paid.


What Is Shimmy?

Shimmy is a single-binary OpenAI-compatible inference server for GGUF models. Point your existing AI tools at Shimmy and they just work — locally, privately, and free.

Shimmy is the server. Airframe is the engine. Under the hood, Shimmy runs on Airframe (v0.4.0), a pure-Rust WebGPU (WGSL) transformer engine. No C++ toolchain, no Python runtime, no backend flags. 26 models certified across 12 families. Version history: CHANGELOG · Airframe CHANGELOG.

Why this matters:

  • No Python runtime or C++ toolchain — Rust only, top to bottom
  • F32 accumulation precision with deterministic output (same model + seed + params → same output)
  • WGSL compute shaders via WebGPU — NVIDIA, AMD, Intel, integrated GPUs, Apple Silicon
  • Model spec auto-derived from GGUF metadata — no hardcoded per-model constants
  • YaRN RoPE scaling for extended context via SHIMMY_MAX_CTX (see Extended Context)

Supported Models

12 model families · 26 certified model/quant combinations — every model below passes Shimmy's 3-box certification regimen (MATH + INFERENCE + DETERMINISM) against the certification ledger. Certification applies to the named model/quant combination; architecture recognition does not automatically mean certification. GGUF files load as-is; no recompilation, no hardcoded per-model constants.

Family Model Quants Llama Llama-3.2-1B-Instruct Q4_K_M · Q6_K Llama-3.2-3B-Instruct Q4_K_M Llama-3.1-8B-Instruct Q4_K_M TinyLlama-1.1B-Chat Q4_0 · Q5_K_M · Q6_K Qwen3 Qwen3-0.6B Q4_K_M Qwen3-1.7B Q4_K_M Qwen3-4B Q4_K_M Qwen3-4B-Thinking Q4_K_M Qwen3-8B Q4_K_M Qwen2 Qwen2-0.5B-Instruct Q4_K_M Qwen2-1.5B-Instruct Q4_K_M Qwen2-7B-Instruct Q4_K_M Qwen3.5 Qwen3.5-9B Q4_K_M Phi-3 Phi-3.5-mini-Instruct Q4_K_M Phi-3-mini-4k-Instruct Q4_0 Phi-2 Phi-2 Q4_K_M Gemma-2 Gemma-2-2B-it Q4_K_M Gemma-2-9B-it Q4_K_M (supported; cert: see v2-roadmap) Gemma-4 Gemma-4-12B-coder Q4_K_M Gemma-4-E4B Q4_K_M DeepSeek-R1 DeepSeek-R1-0528-Qwen3-8B Q4_K_M Ministral Ministral-3-14B-Reasoning Q4_K_M StarCoder2 StarCoder2-3B Q4_K_M

SafeTensors format (.safetensors) is supported for model loading via safetensors_native. Full Airframe-native inference for SafeTensors remains roadmap work; see docs/v2-roadmap.md.

Features

  • ⚡ TurboShimmy INT4 KV Cache — About 7× lower KV-cache memory in tested configurations. Run Llama-3.2-3B on 4 GB GPUs.
  • ** OpenAI SDK Compatibility** — Chat completions, text completions, streaming, and model endpoints. Works with OpenAI SDKs and tools using that surface.
  • ** Extended Context** — YaRN RoPE scaling via SHIMMY_MAX_CTX.
  • ** Migrating from v1.x** — llama.cpp, MLX, HuggingFace, and RustChain backends removed in v2.0+. Shimmy is now a pure Airframe product.
  • ** Certification** — Every model passes a 3-box certification regimen (MATH + INFERENCE + DETERMINISM). See docs/CERTIFICATION.md.
  • ** MOE support** — Mixture-of-Experts CPU offloading is on the Airframe roadmap.

Quick Start

cargo install shimmy
shimmy serve --model-path /absolute/path/to/model.gguf --bind 127.0.0.1:11435

Then in another terminal:

shimmy list --short
curl -s http://127.0.0.1:11435/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"tinyllama-1.1b","messages":[{"role":"user","content":"Say hi in 5 words."}],"max_tokens":32}'

Full install, model acquisition, GPU, VRAM sizing, platform-specific builds: docs/quickstart.md


Documentation

Start here What you need Quick Start Install, models, GPU, VRAM Supported Models Certified models and quantization API Compatibility Endpoints, SDKs, integration Configuration Env vars and config options Troubleshooting GPU errors, model failures Complete documentation index Section Documents Models & Performance TurboShimmy — INT4 KV cache compression · Extended Context — YaRN RoPE scaling, VRAM math · Performance — Tuning and token/sec · Model Expansion — Onboarding protocol API & Integration API Reference · OpenAPI / Swagger UI · Integration Guides · Examples · Cross-Compilation Engine Architecture · GPU Pipeline · Quantization · Chat Templates Certification Certification · Methodology · Regression Testing · PPT Testing · Metrics FAQ FAQ · Features · Migration · Windows GPU

Development Testing

Shimmy maintains high code quality through comprehensive testing:

# Full test suite (default features = GPU engine)
cargo test --features airframe,huggingface

# Quick CPU-only tests (no GPU required)
cargo test --lib --no-default-features --features huggingface -- --test-threads=1

See docs/ppt-invariant-testing.md for technical details.


Community & Support

  • ** Bug Reports**: GitHub Issues
  • ** Discussions**: GitHub Discussions
  • ** Security**: Security Policy

Momentum Snapshot

** stars and climbing fast** ⏱ <1s startup 100% Rust, no Python

As Featured On

Hacker News · Front Page Again · IPE Newsletter

Companies: Need invoicing? Email [email protected]


Performance

Tool Startup Memory API Shimmy <1s ~50MB Chat, completions, streaming, models Ollama 5-10s 200MB+ Partial

Measured on RTX 3060, Shimmy v2.6.0, TinyLlama-1.1B. Your results vary by hardware.


Sponsor Shimmy

Shimmy is independently maintained. Sponsorship funds certification, compatibility work, and releases.

  • $5/month: Coffee tier ☕ — Sponsor badge + name in SPONSORS.md
  • $25/month: Supporter — Priority support + name in SPONSORS.md
  • $100/month: Corporate backer — Logo placement + release recognition
  • $500/month: Infrastructure partner — Office hours + roadmap consultation

Current sponsors: ZephyrCloudIO · alistairheath

** Become a Sponsor** · Invoicing


License & Philosophy

MIT License — see LICENSE. Shimmy will be free forever.

Promise: This will never become a paid product.

Shimmy is infrastructure: it should be invisible. Reliability through comprehensive validation and property-based testing.


Maintainer: Michael A. Kuykendall · Mission: Making local model inference simple and reliable


Support

This project is a safe space. Trans rights are human rights.

If you or someone you love needs support:

  • The Trevor Project — 24/7 for LGBTQ+ young people. Call 1-866-488-7386 or text START to 678-678
  • Trans Lifeline — peer support run by and for trans people. US: 877-565-8860
  • 988 Suicide & Crisis Lifeline — call or text 988

GitHub Issues· 0 open

View all on GitHub

No open issues yet, or sync has not completed.

Highlights

  • •No Python runtime or C++ toolchain — Rust only, top to bottom
  • •F32 accumulation precision with deterministic output (same model + seed + params → same output)
  • •WGSL compute shaders via WebGPU — NVIDIA, AMD, Intel, integrated GPUs, Apple Silicon
  • •Model spec auto-derived from GGUF metadata — no hardcoded per-model constants
  • •YaRN RoPE scaling for extended context via SHIMMY_MAX_CTX (see Extended Context)
  • •⚡ TurboShimmy INT4 KV Cache — About 7× lower KV-cache memory in tested configurations. Run Llama-3.2-3B on 4 GB GPUs.
  • •OpenAI SDK Compatibility — Chat completions, text completions, streaming, and model endpoints. Works with OpenAI SDKs and tools using that surface.
  • •Extended Context — YaRN RoPE scaling via SHIMMY_MAX_CTX.
  • •Migrating from v1.x — llama.cpp, MLX, HuggingFace, and RustChain backends removed in v2.0+. Shimmy is now a pure Airframe product.
  • •Certification — Every model passes a 3-box certification regimen (MATH + INFERENCE + DETERMINISM). See docs/CERTIFICATION.md.

> Tags

Rustapi-servercommand-line-tooldeveloper-toolsgguf

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> Details

PublishedAug 1, 2026
UpdatedSep 17, 2026
Category数据库
PricingOpen source

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