A blazingly fast, native Swift inference server that serves MLX models with a strict OpenAI-compatible API.

No Python runtime, no Global Interpreter Lock (GIL), no unnecessary memory copies. Just bare-metal Apple Silicon performance compiled to a single binary.

• 🍎 100% Native Apple Silicon: Powered natively by Metal and Swift.
• 🔌 OpenAI-compatible: Drop-in replacement for OpenAI SDKs (/v1/chat/completions, streaming, etc).
• 🧠 Smart Model Routing: Loads HuggingFace format models directly, with native Safetensors parsing.
• ⚡️ TurboQuantization Integrated: Custom low-level MLX Metal primitives that apply extremely fast quantization for KV caching out-of-the-box.
• 💾 SSD Expert Streaming: Experimental zero-copy streaming that swaps Mixture of Experts (MoE) layers directly from the NVMe SSD to the GPU command buffer without trashing macOS Unified Memory (prevents Watchdog OS kernel panics on 122B+ models).
• 🎛️ Granular Memory Control: Integrated Layer Partitioning (--gpu-layers) and Wisdom Auto-Calibration for squeezing massive models into RAM.

SwiftLM implements a hybrid V2+V3 TurboQuant architecture for on-the-fly KV cache compression. At roughly ~3.6 bits per coordinate overall, the KV cache is compressed ~3.5× vs FP16 with near-zero accuracy loss.

Recent reproductions of the TurboQuant algorithm (e.g., turboquant-mlx) revealed two distinct paths:

1. V2 (Hardware-Accelerated): Fast, but uses linear affine quantization which degrades quality at 3-bit.
2. V3 (Paper-Correct): Excellent quality using non-linear Lloyd-Max codebooks, but painfully slow due to software dequantization.

We built the "Holy Grail" hybrid: We ported the V3 non-linear Lloyd-Max codebooks directly into the native C++ encoding path, and process the dequantization natively in fused Metal (bggml-metal) shaders. This achieves V3 quality at V2 speeds, completely detached from Python overhead.

K-Cache (3-bit PolarQuant + 1-bit QJL) = 4.25 bits/dim

1. Extract L2 norm and normalize: x̂ = x / ‖x‖
2. Apply Fast Walsh-Hadamard Transform (WHT) rotation to distribute outliers evenly.
3. Quantize each coordinate using 3-bit non-linear Lloyd-Max centroids.
4. Compute the residual error between the original vector and the quantized approximation.
5. Project the residual via a random Johnson-Lindenstrauss (QJL) matrix and store the 1-bit signs. (Why QJL? QJL acts as an additional regularizer that prevents centroid resolution loss from degrading the attention dot-product.)

V-Cache (3-bit PolarQuant) = 3.125 bits/dim Because the V-cache matrix is not used for inner-product attention scoring, the QJL error correction provides no benefit. We cleanly disable QJL for the V-cache, extracting an additional 25% memory savings without sacrificing quality.

Reference implementations: turboquant-mlx | turboquant_plus | Paper: TurboQuant, Google 2504.19874

To reliably run massive 122B parameter MoE models over SSD streaming, SwiftLM was designed and benchmarked natively on the following hardware:

• Machine: MacBook Pro, Apple M5 Pro
• Memory: 64 GB Unified Memory
• Model: Qwen3.5-122B-A10B-4bit
• SSD: Internal Apple NVMe (Zero-Copy Streaming)

⚠️ Quantization Disclaimer: While heavier quantization shrinks the required memory footprint, 4-bit quantization remains the strict production standard for MoE models. Our metrics indicated that aggressive 2-bit quantization heavily destabilizes JSON grammars—routinely producing broken keys like \name\ instead of "name"—which systematically breaks OpenAI-compatible tool calling.

A native iPhone & iPad companion app that downloads MLX models directly from HuggingFace and runs inference on-device via MLX Swift.

• Tab UI: Chat · Models · Settings
• Live download progress with speed indicator and circular progress ring
• Model catalog: Qwen3, Phi-3.5, Mistral, Llama — with on-device RAM fit indicators
• HuggingFace search — find any mlx-community model by name
• Context-aware empty states — downloading ring, loading spinner, idle prompt
• iOS lifecycle hardened — model unload only fires on true background (not notification banners); 30-second grace period on app-switch

📱 Running live on iPhone 13 Pro (6 GB) — no Python, no server, no GIL. Pure on-device MLX inference via Metal GPU.

cd SwiftLMChat
python3 generate_xcodeproj.py       # Generates SwiftLMChat.xcodeproj
open SwiftLMChat.xcodeproj

Then in Xcode:

1. Select the SwiftLMChat target → Signing & Capabilities
2. Set your Team (your Apple Developer account)
3. Select your iPhone as the run destination
4. ⌘R to build and run

Note for contributors: The .xcodeproj is git-ignored (it contains your personal Team ID). Run generate_xcodeproj.py after cloning to regenerate it locally. Your Team ID is never committed.

Download the latest release tarball from the Releases page. The archive is self-contained — default.metallib is bundled alongside the binary.

tar -xzf SwiftLM-<version>-macos-arm64.tar.gz

# Run from the extracted directory — default.metallib must be co-located with the binary
./SwiftLM --model mlx-community/Qwen2.5-3B-Instruct-4bit --port 5413

⚠️ Metal GPU Error? If you see Failed to load the default metallib, it means default.metallib is missing from the directory you are running SwiftLM from. Make sure you run the binary from the extracted folder and do not move the binary without also moving default.metallib alongside it.

# Must clone recursively — default.metallib ships inside the mlx-swift submodule
git clone --recursive https://github.com/SharpAI/SwiftLM
cd SwiftLM
swift build -c release

default.metallib is a pre-built artifact inside the mlx-swift submodule, version-matched to the Swift binary. Copy it next to the binary before running:

cp LocalPackages/mlx-swift/Source/Cmlx/mlx/mlx/backend/metal/kernels/default.metallib \
   .build/release/

.build/release/SwiftLM \
  --model mlx-community/Qwen3.5-122B-A10B-4bit \
  --stream-experts \
  --port 5413

⚠️ Do NOT use Python's mlx-metal package as a source for mlx.metallib.
While uv run --with mlx-metal python -c "...shutil.copy(metallib, ...)" will get the server to start, the pip mlx-metal package is a different version of MLX than what this binary was compiled against. The version mismatch causes GPU kernel ABI corruption during inference, producing a freed pointer was not the last allocation crash. Always use the metallib from LocalPackages/mlx-swift/ — it is the only version-matched artifact for this build.

(Add --stream-experts when running oversized MoE models like Qwen3.5 122B to bypass macOS virtual memory swapping and stream expert layers directly from NVMe.)

Drop-in compatible with standard OpenAI HTTP consumers:

curl http://localhost:5413/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen3.5-122B-A10B-4bit",
    "stream": true,
    "messages": [
      {"role": "system", "content": "You are Aegis-AI, a local home security agent. Output strictly in JSON format."},
      {"role": "user", "content": "Clip 1: Delivery person drops package at 14:02. Clip 2: Delivery person walks away down driveway at 14:03. Do these clips represent the same security event? Output a JSON object with a `duplicate` boolean and a `reason` string."}
    ]
  }'

• macOS 14.0+
• Apple Silicon (M1/M2/M3/M4/M5)
• Xcode Command Line Tools
• Metal Toolchain (xcodebuild -downloadComponent MetalToolchain)

Built entirely on the hard work of the Apple MLX community.

• mlx-swift — Apple MLX framework for Swift
• Hummingbird — Event-driven Swift HTTP server
• flash-moe — Reference for SSD Expert Streaming

The TurboQuant KV cache compression implemented in SwiftLM is directly based on the following open-source work and research:

• TheTom/llama-cpp-turboquant — The primary reference for the C and Metal GPU implementation. The turbo-wht.h Fast Walsh-Hadamard kernel, WHT sign arrays (seed=42), Lloyd-Max centroid tables, and the ggml-turbo-quant.c quantize/dequantize logic were ported directly from this repository into our MLX C++ and Metal backend.

• TheTom/turboquant_plus — Python reference implementation used to validate the algorithm math, codebook construction (Lloyd's algorithm for N(0, 1/d)), and KV cache integration design.

• TurboQuant Paper — "TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate", Zandieh et al., AISTATS/ICLR 2026. The two-stage PolarQuant + QJL algorithm described in Section 3 and Appendix A is the mathematical foundation of this implementation.

• amirzandieh/QJL — Original Quantized Johnson-Lindenstrauss (QJL) 1-bit residual correction implementation by the paper authors.

MIT License