MLXServer/CLAUDE.md

MLX Server

Native macOS SwiftUI app for local LLMs on Apple Silicon via MLX. Provides a chat UI and an embedded OpenAI-compatible API server. Supports vision and tool use.

Quick Start

# Build (requires xcodegen: brew install xcodegen)
./build.sh

# Run
open "build/Debug/MLX Server.app"

Project Structure

• MLXServer/MLXServerApp.swift — App entry point, GPU cache config
• MLXServer/ContentView.swift — Main layout, toolbar, keyboard shortcuts
• MLXServer/Models/ModelConfig.swift — Model definitions (alias, repoId, contextLength), resolution
• MLXServer/Models/ChatMessage.swift — Chat message data model
• MLXServer/ViewModels/ModelManager.swift — Model loading/switching via VLMModelFactory, offline-first resolution
• MLXServer/ViewModels/ChatViewModel.swift — Chat state, ChatSession management, API server lifecycle
• MLXServer/Server/APIServer.swift — NWListener HTTP server, SSE streaming, KV cache reuse, vision, tool call handling
• MLXServer/Server/APIModels.swift — OpenAI-compatible Codable structs
• MLXServer/Server/ToolCallParser.swift — Parses tool calls from model output (Gemma tool_code, Qwen XML tags)
• MLXServer/Server/ToolPromptBuilder.swift — Model-specific tool prompt formatting
• MLXServer/Utilities/LocalModelResolver.swift — Resolves HF repo IDs to ~/.cache/huggingface/hub/ snapshots
• MLXServer/Utilities/Preferences.swift — UserDefaults wrapper
• project.yml — xcodegen project spec
• build.sh — Build script (xcodegen + xcodebuild)

Supported Models

Alias	HuggingFace ID	Notes
gemma	mlx-community/gemma-3-4b-it-4bit	Vision + tool use via tool_code blocks (128k context)
qwen	mlx-community/Qwen3-VL-4B-Instruct-4bit	Vision + tool use via <tool_call> tags (256k context)

Critical Performance Rule

Inference speed is the #1 priority. The token generation loop must never be blocked or slowed by anything else — no MainActor hops, no SwiftUI observation, no synchronous I/O. Everything that isn't inference (stats collection, UI updates, logging) must run on separate threads via loose coupling:

• LiveCounters (thread-safe singleton with OSAllocatedUnfairLock) is the bridge: generation code writes to it directly from any thread with zero actor overhead.
• InferenceStats (UI-side, @Observable @MainActor) polls LiveCounters at 1Hz via a timer — never the other way around.
• SSE streaming (sendSSEEvent/sendData) runs nonisolated off MainActor so token sends don't compete with SwiftUI rendering.
• Never gate token output on UI state, analytics, or any @MainActor-isolated code.

Key Design Decisions

• Uses mlx-swift-lm (MLXVLM / VLMModelFactory) as the inference backend — supports both text and vision in a single model load
• Model-specific prompt formatting: Gemma uses tool_code blocks; Qwen uses <tool_call> XML tags
• Offline-first: if the model is already cached locally (~/.cache/huggingface/hub/), LocalModelResolver resolves the local snapshot path directly — no network requests
• HTTP server built on Network.framework (NWListener) — no third-party server dependencies
• KV cache reuse across API requests — reuses ChatSession when conversation history prefix matches
• GPU cache limit set to 20 MB; cache cleared on model unload

Dependencies

Managed via Swift Package Manager (declared in project.yml for xcodegen).

Package	Products
mlx-swift-lm	MLXLLM, MLXVLM, MLXLMCommon
swift-markdown-ui	MarkdownUI