On-Device AI for iOS & macOS A new Swift tutorial demonstrates how to run large language models directly on iOS and macOS devices using the NobodyWho library, which wraps llama.cpp in Rust and provides a clean Swift API. The tutorial covers streaming responses, model downloads from Hugging Face, and advanced features like multimodal input and tool calling, highlighting the benefits of on-device AI such as privacy, offline capability, and cost savings. In this Swift tutorial, you'll learn how to run a large language model LLM directly on a user's device: no server, no API key needed. We'll start from scratch with a simple chat exchange, and progressively introduce more advanced features: multimodal input, speech-to-text, text-to-speech, voice activity detection, tool calling and RAG. Each concept is explained before the code, so you can follow along whether you're new to on-device AI. Most AI features rely on a cloud API: you send a request to a remote server, it runs the model, and sends a response back. That works well, but it comes with tradeoffs. Running the model directly on the device avoids all of them: The tradeoff is raw capability: on-device models are smaller and less powerful than frontier cloud models. But for many use cases like summarization, chatbots, or local search, they're more than good enough. We'll use the NobodyWho https://github.com/nobodywho-ooo/nobodywho library throughout this tutorial. It wraps llama.cpp https://github.com/ggerganov/llama.cpp in Rust and exposes a clean Swift API for running any model locally in .gguf format, across iOS, macOS, visionOS, and watchOS. Add it with Swift Package Manager. In Xcode, go to File → Add Package Dependencies and enter: https://github.com/nobodywho-ooo/nobodywho-swift.git Or add it to your Package.swift : dependencies: .package url: "https://github.com/nobodywho-ooo/nobodywho-swift.git", from: "2.1.0" NobodyWho can download a GGUF model for you directly from Hugging Face, cache it, and reuse it on every subsequent launch. That means you don't need to bundle anything into your app or manage downloads yourself: python import NobodyWho let chat = try await Chat.fromPath modelPath: "hf://NobodyWho/Qwen Qwen3-0.6B-GGUF/Qwen Qwen3-0.6B-Q4 K M.gguf" The first time this runs, the model is downloaded to the platform cache directory. Every call after that loads the model directly. modelPath accepts a few different forms: | Form | Example | Notes | |---|---|---| | HuggingFace reference | hf://owner/repo/file.gguf | Downloaded and cached on first use | | HTTPS URL | https://example.com/model.gguf | Downloaded and cached on first use | | Local path | /path/to/model.gguf | Used as-is, no download | The HuggingFace prefix is case-insensitive, so hf:// and huggingface:// are equivalent. You can also track a remote download by passing a progress closure to Chat.fromPath , which receives downloaded, total byte counts and is skipped for cached or local files: js let chat = try await Chat.fromPath modelPath: "hf://NobodyWho/Qwen Qwen3-0.6B-GGUF/Qwen Qwen3-0.6B-Q4 K M.gguf" { downloaded, total in print "Downloaded \ downloaded /\ total bytes" } You can find thousands of LLM in .gguf format on Hugging Face here https://huggingface.co/models?library=gguf&sort=trending . With a model loaded, you're ready to start a conversation: js let chat = try await Chat.fromPath modelPath: "hf://NobodyWho/Qwen Qwen3-0.6B-GGUF/Qwen Qwen3-0.6B-Q4 K M.gguf" let response = try await chat.ask "Is water wet?" .completed print response // Yes, indeed, water is wet chat.ask sends your message and returns a TokenStream , which conforms to AsyncSequence . Calling .completed waits for the whole response and gives you back the final string, which is fine for a one-off question. But a real chat interface needs to stream tokens as they arrive, otherwise users stare at a blank screen until generation finishes. js let response = chat.ask "What is the capital of Denmark?" for await token in response { print token } A token is the smallest unit a model generates, typically a word, or a fragment of a word. If you need to cancel a response mid-generation, for example when the user taps a "Stop" button, call chat.stopGeneration . It's synchronous and safe to call from any thread; the tokens already produced stay in the stream and are kept in the chat history, so the conversation stays coherent. Some models can natively ingest images and audio. To use them, you need two things: a multimodal LLM, and its projection model that converts images and/or audio into tokens the LLM can consume usually named with mmproj in it . A solid default that handles both image and audio is Gemma 4 with its BF16 projection model. python import NobodyWho let chat = try await Chat.fromPath modelPath: "/path/to/vision-model.gguf", projectionModelPath: "/path/to/mmproj.gguf" To actually send image or audio content, build a Prompt mixing text, images, and audio, and pass it to chat.ask instead of a plain string: js let prompt = Prompt Prompt.text "Tell me what you see in the image and what you hear in the audio." , Prompt.image "/path/to/dog.png" , Prompt.audio "/path/to/sound.mp3" , let response = try await chat.ask prompt .completed Keep in mind that images and audio consume context fast, so you'll likely want a bigger contextSize than you'd use for text-only chat. Also note that the language model and its projection model have to be trained together. If you'd rather transcribe spoken audio into text than have the model listen to it directly, NobodyWho integrates Whisper models in ONNX format through SpeechToText . python import NobodyWho let stt = try await SpeechToText.load source: "hf://onnx-community/whisper-base" let text = try await stt.transcribeFile path: "recording.mp3" .completed print text source is a Hugging Face repo hf://owner/repo or a local directory laid out the same way. Browse the Whisper ONNX models on Hugging Face https://huggingface.co/models?library=onnx&search=whisper to find one that fits your accuracy and speed needs. If your audio comes from a buffer rather than a file, use transcribePcm : js let text = try await stt.transcribePcm samples: samples, sampleRate: 16000 .completed The buffer needs to be mono i16 PCM samples. The sample rate can be anything, NobodyWho resamples internally to what Whisper expects. And just like chat, transcription can be streamed piece by piece instead of waiting for the full result: for try await piece in stt.transcribeFile path: "recording.mp3" { print piece } Going the other direction, TextToSpeech turns text into WAV audio you can play back or save. python import Foundation import NobodyWho let tts = try await TextToSpeech.load source: "hf://NobodyWho/Kokoro-82M", voice: "bf emma", language: "en-gb" let wav = try await tts.synthesize "Hello from NobodyWho " try wav.write to: URL fileURLWithPath: "out.wav" Three architectures are supported, all ONNX-based: Kokoro https://github.com/hexgrad/kokoro , Pocket TTS https://github.com/kyutai-labs/pocket-tts , and Supertonic https://github.com/supertone-inc/supertonic . NobodyWho infers which one you're using from the source string, so you only need to set architecture explicitly when loading from a custom local folder. Each architecture has its own voice and language options that need to agree with what the model supports. Before transcribing audio, it helps to know when someone is actually speaking rather than relying on a fixed silence timeout. VoiceActivityDetection uses a small model to reliably tell speech and silence apart, and pairs naturally with SpeechToText . For streaming microphone input, push chunks in as they arrive: python import NobodyWho let vad = try await VoiceActivityDetection.load sampleRate: 16000, source: "hf://onnx-community/silero-vad" let stt = try await SpeechToText.load source: "hf://onnx-community/whisper-base" while let chunk = readMic { if try vad.push chunk: chunk == .speechEnded { break } } let speech = vad.finish let transcription = try await stt.transcribePcm samples: speech, sampleRate: 16000 .completed print transcription Each push call reports the current state .speechStarted , .speechEnded , .speech , or .silence , and finish hands you back the buffered speech segment while resetting internal state for the next turn. If you already have a full recording and just want to pull out the speech segments from it, segment does that in one pass: js let audio = readWavPcm path: "recording.wav" for speech in try vad.segment samples: audio { let transcription = try await stt.transcribePcm samples: speech, sampleRate: 16000 .completed print transcription } Sensitivity is tunable via threshold , minSpeechDurationMs , minSilenceDurationMs , and prerollDurationMs how much audio to keep before the detected start, so you don't clip the beginning of a sentence . The defaults are a reasonable starting point, but VAD is one of those things that usually benefits from tuning to your actual environment. Tools let the model call out to real functions in your app rather than just generating text. The easiest way to create one is with the @DeclareTool macro: annotate any top-level or type-member function with a description, and NobodyWho generates the tool for you. python import NobodyWho @DeclareTool "Calculates the area of a circle given its radius" func circleArea radius: Double - String { let area = Double.pi radius radius return "Circle with radius \ radius has area \ String format: "%.2f", area " } let chat = try await Chat.fromPath modelPath: "hf://NobodyWho/Qwen Qwen3-0.6B-GGUF/Qwen Qwen3-0.6B-Q4 K M.gguf", tools: circleAreaTool The macro names the generated variable