Backburner

Swiftlet is an open-source Swift and Metal runtime for running Qwen3-Next and Qwen3.5/3.6 mixture-of-experts models locally on Apple devices, including iPhones. It keeps the models' small dense core in memory and streams routed expert weights from storage on demand, allowing 35B and 80B models to run with comparatively low RAM. The project is available as a Swift package, command-line interface, OpenAI-compatible server, and iOS app; its repository describes the runtime as working end to end and notes that only about 3B parameters are active per token in the supported models.

slotstream is an open-source macOS local-LLM inference engine that runs Qwen3.8-Flash-Next, a 125-billion-parameter mixture-of-experts model, on Apple Silicon Macs whose memory cannot hold the model. Implemented as a single Swift binary using MLX, it keeps the dense trunk resident and reads routed expert weights from an SSD into a shared cache of slots, sizing that cache to available memory instead of loading the full model. It supports prefix caching for follow-up turns and exposes Ollama-compatible and OpenAI-compatible chat APIs, as well as a command-line prompt mode; the documented API subset supports streaming, CORS, and sampling options but not tools, images, JSON-schema output, or logprobs. The project requires macOS 14 or later, Apple Silicon, and roughly 110 GB of free disk space for the model weights. Its installer provides signed release binaries, and the repository can also be built with Swift and the macOS Command Line Tools. The source is MIT-licensed; the model weights are distributed under the Qwen community license.

oMLX is an open-source LLM inference server for Apple Silicon Macs, managed through a macOS menu-bar application. It serves text, vision, OCR, embedding, and reranking models through compatible API endpoints, with model discovery and management, monitoring, continuous batching, and caching. Its caching system keeps KV cache in a hot in-memory tier and a cold SSD tier, allowing cached context to remain reusable across requests even when the conversation context changes. The project can be installed as a macOS app, through Homebrew, or from source, and includes a CLI for controlling the app-managed server. The repository states that it requires macOS 15 or later, Python 3.11–3.13, and Apple Silicon hardware.

TurboFieldfare is a model-specific Swift and Metal runtime for running the instruction-tuned Gemma 4 26B-A4B model on Apple Silicon Macs, including systems with 8 GB of RAM. It keeps the model’s shared 1.35 GB core and FP16 key-value cache in memory while streaming the expert weights selected for each token from an SSD, allowing inference with about 2 GB of resident weights instead of loading the approximately 14.3 GB model. The model uses MLX affine 4-bit weights with group size 64, an 8-bit router, 4-bit shared and routed experts, and about 3.88 billion active parameters per token. The project includes a native macOS app, command-line interface, Swift runtime library with Metal kernels, decode service, streaming model installer and verifier, and experimental loopback OpenAI-compatible Chat Completions server. These products share a repacked `.gturbo` model directory; the installer streams byte ranges from a pinned Hugging Face revision, repacks them without materializing the source checkpoint, and validates the resulting manifest and file hashes. The app and CLI support instruction and completion generation with optional system guidance but do not execute tools; the server accepts function-tool declarations and returns model-produced tool calls for client authorization and execution. An optional companion vision tower adds image input on M2 or newer Macs, while text-only inference remains available on M1. The package is arm64-only and targets macOS 26, Metal 4, and Swift 6.2 or newer, with approximately 14.3 GB of storage for the text model plus about 1.1 GB for the image pack.

vphone-cli is an open-source macOS command-line tool for creating and running virtual iPhones with Apple's Virtualization.framework and PCC research VM infrastructure. It targets Apple Silicon Macs running macOS 15 or later and automates the VM workflow from IPSW download and patching through DFU restore, custom firmware installation, and first boot. The tool supports multiple firmware patch variants, VM creation and management, SSH and VNC connections, IPA installation, and export/import or APFS cloning of VM bundles. Its host control socket provides programmatic screenshots, touch and swipe input, hardware-key events, and clipboard operations, with each action returning an inline screenshot; the repository also describes an MCP server named vphone-mcp for AI-driven end-to-end testing. Operation requires Xcode and the iOS SDK, several Homebrew dependencies, and macOS SIP/AMFI relaxation to permit the required private virtualization entitlements.