SwiftLM：Apple Silicon原生高性能LLM推理服务器

SwiftLM is a native Swift large language model inference server built for Apple Silicon, based on MLX Swift. It features OpenAI-compatible API, SSD streaming for ultra-large MoE models, TurboQuant KV cache compression, and an iOS companion app. Key advantages include no Python runtime overhead, Metal GPU acceleration, and industry-leading local inference performance on macOS/iOS devices.

SwiftLM is a native Swift LLM inference server designed exclusively for Apple Silicon. It eliminates Python runtime and GIL overhead by compiling to a single binary, achieving bare-metal performance. Built on Apple's MLX framework, it leverages Metal GPU acceleration for top-tier local inference on macOS and iOS.

Unlike Python-based solutions, SwiftLM is deeply optimized for Apple hardware. It supports billions of parameters and breaks through consumer hardware limits with SSD streaming for over 1000B parameter MoE models.

SwiftLM's tech stack focuses on native Apple Silicon support (direct Metal/Swift calls), strict OpenAI API compatibility (seamless SDK replacement), and smart model routing (HuggingFace format, Safetensors support). It integrates TurboQuantization to solve memory bottlenecks via custom MLX Metal primitives for fast KV cache quantization.

TurboQuant balances V3-level quality and V2-level speed. It uses two-stage quantization:

• K-Cache: 3-bit PolarQuant +1-bit QJL (4.25 bits/dimension). Steps: L2 norm normalization → WHT rotation → 3-bit Lloyd-Max quantization → 1-bit JL projection for residual.
• V-Cache: 3-bit PolarQuant (no QJL, 3.125 bits/dimension) saves 25% memory without quality loss.

For ultra-large MoE models (e.g., Qwen3.5-122B), SwiftLM uses zero-copy SSD streaming to send expert layers directly to GPU command buffers, bypassing macOS unified memory to avoid Watchdog kernel panic.

Tested on M5 Pro (64GB RAM), it runs Qwen3.5-122B-A10B-4bit. 4-bit quantization is the production standard—2-bit quant breaks JSON syntax and tool calls.

SwiftLM has a native iPhone/iPad app that runs MLX models without Python. Features:

• UI: Chat/Model/Settings tabs, real-time download progress.
• Model support: Qwen3, Phi-3.5, Mistral, Llama (RAM适配 indicator).
• Lifecycle optimization: Unloads models only when app enters background (30s grace period). Runs smoothly on iPhone13 Pro (6GB RAM).

Deployment options:

• Precompiled: Download from Releases (bundled default.metallib).
• Source build: Recursive clone to get mlx-swift submodule.

Commands: Basic (specify model/port), --stream-experts for large MoE. Supports OpenAI API features (chat completion, streaming, multi-turn, system prompts). Integrates with Continue.dev, LangChain, Open WebUI, etc.

M5 Pro benchmarks show strong performance for 122B models. Key optimizations: --gpu-layers (limit GPU layers), --stream-experts (MoE streaming).

Troubleshooting: Metal GPU error handling, API mode diagnosis, GitHub plugin auth configuration.