The best AI SDKs for on-device inference in 2026

On-device AI stopped being a research demo and became a shipping decision. Phones, watches, headsets, and laptops now have enough compute to run capable language, speech, and vision models locally. It means you can build AI features that work offline, keep user data on the device, and cost nothing per request.

The hard part is no longer whether you can run a model on-device. It's picking the SDK that gets you from .gguf file to a working feature in the language you actually ship in, without giving up privacy, speed, or the features you need.

This guide compares the best AI SDKs for on-device inference in 2026, with a side-by-side competitor table and an honest look at what each one is good at.

Why run AI on-device?

Most AI features today route every request through a hosted API: you send data to a remote server, it runs the model, and sends a response back. That works, but it comes with tradeoffs that get worse as you scale.

Running the model directly on the device avoids them:

• Privacy by design — user data never leaves the device
• Works offline — no internet connection required, anywhere
• Low latency — no network round-trip on every interaction
• No cloud costs — inference is free, with no per-token bill
• No infrastructure — no servers to provision, scale, or babysit

The tradeoff is raw capability, a local model is smaller than a frontier cloud model, but for chat, summarization, classification, transcription, structured extraction, and many agentic workflows, a good 1B–8B model running locally is more than enough.

What to look for in an on-device AI SDK

Not every SDK that can run a model on a device is a good fit for shipping a real product. When you're evaluating one, look for:

• Cross-platform bindings — does it support the language and framework you actually build in (Swift, Kotlin, Flutter, React Native, Python, Godot)?
• A fast, portable runtime — GPU acceleration (Metal, Vulkan) and CPU fallback so it runs well across the whole device fleet.
• Model format support — broad compatibility (e.g. GGUF) so you can use thousands of open-weight models instead of a walled garden.
• More than just LLMs — text-to-speech, speech-to-text, voice activity detection, and multimodal input, so a voice assistant doesn't require five separate libraries.
• Developer ergonomics — streaming, tool calling, structured output, embeddings/RAG, and model downloading built in.
• A sane license and open source — so you can audit it, trust it, and ship it without surprises.

Competitor comparison

The best AI SDKs for on-device inference

1. NobodyWho

NobodyWho is an inference engine for running AI models locally and efficiently. A single, well-maintained Rust core, wrapping the excellent llama.cpp. It supports several languages and frameworks Swift, Kotlin, Flutter, React Native / Expo, Python, and Godot with a consistant API naming.

What makes it stand out for on-device work specifically:

• Runs fully offline and free — no API keys, no servers, no hidden fees
• A complete on-device stack, not just an LLM — text-to-speech (Kokoro, Pocket-tts, Supertonic), speech-to-text (Whisper), and voice activity detection (Silero VAD) are first-class, so you can build a full local voice assistant without gluing five libraries together.
• Multimodal input — feed images and audio to your model.
• Type-safe tool calling — it generates structured grammars from your function signatures automatically, so you never hand-write a JSON schema.
• Fast and efficient — GPU-accelerated inference via Metal and Vulkan, with conversation-aware preemptive context shifting so you keep full conversation memory without message-length limits.
• Any GGUF model — Gemma, Qwen, Mistral, Llama, and thousands more, loaded directly from Hugging Face or any URL.
• Genuinely open source — EUPL v1.2, a good open-source citizen with 500+ closed PRs.

It's also proven in production: the team ships free, open-source apps built entirely on NobodyWho, including NobodyWho Chat (iOS/Android), NobodyWho Wrist the first app to run an LLM entirely on an Apple Watch, and NobodyWho Eyes for Vision Pro.

Best for: developers who want efficient, private, cross-platform on-device & complete AI stack.

2. llama.cpp

The reference C/C++ engine that most of the on-device world is built on, including NobodyWho. It defines the GGUF format, supports Metal/Vulkan/CUDA, and runs an enormous range of quantized models efficiently on CPUs and GPUs.

Strengths: unmatched model coverage, excellent performance, active community.

Limitations: it's a low-level engine, not an app SDK. You get the runtime; you build the bindings, the voice/vision stack, tool calling, and the ergonomics yourself. Great as a foundation, a lot of work as a product SDK.

3. Google AI Edge / LiteRT (MediaPipe)

Google's on-device stack — LiteRT (formerly TensorFlow Lite) plus MediaPipe's LLM Inference API — with hardware delegates (NNAPI, GPU) and a large ecosystem for vision and classic ML.

Strengths: mature tooling, strong quantization and hardware acceleration, excellent for computer-vision and classical-ML workloads.

Limitations: the LLM side is newer and narrower, model conversion can be fiddly, and it doesn't offer a unified LLM+voice+vision developer experience across every framework.

4. ONNX Runtime Mobile

A compact, portable runtime that executes ONNX graphs with pluggable execution providers across CPU, GPU, and NPUs.

Strengths: framework-agnostic, small footprint, solid building blocks for CV, audio, and NLP.

Limitations: it's infrastructure, not an on-device LLM/voice product. Getting good mobile LLM ergonomics — streaming, tool calling, context management — requires meaningful extra work.

5. Apple MLX

Apple's array framework for machine learning on Apple silicon, with Swift and Python APIs and a growing collection of ported models.

Strengths: beautifully tuned for Apple hardware, great for experimentation and research.

Limitations: Apple-only by design — no Android, no cross-platform story — and lower-level than a batteries-included app SDK.

6. RunAnywhere

An SDK that pairs an on-device runtime with hybrid cloud routing, OTA model distribution, and fleet management for LLM, speech (STT/TTS/VAD), and vision models, with bindings for React Native, Flutter, Kotlin, Swift, and Web.

Strengths: cross-platform SDKs, hybrid on-device/cloud routing, and operational controls like model rollouts and observability.

Limitations: cloud routing, over-the-air model updates, and telemetry run through RunAnywhere's own hosted backend, with no self-hosting option, so you're tied to their cloud for the operational side. Its licence isn't truly open source either: it's Apache 2.0 with a commercial-use cap, free only for individuals, non-profits, and organisations under $1M in both funding and annual revenue.

7. Cactus

Cactus is a hybrid inference engine for mobile and edge, with cross-platform SDKs covering LLMs, vision, speech-to-text, VAD, and embeddings, plus optional cloud fallback.

Strengths: low setup, straightforward integration, and real on-device NPU acceleration across major mobile chipsets (Apple, Snapdragon, Google, Exynos, MediaTek).

Limitations: it uses its own proprietary .cact model format rather than GGUF, so models have to be converted with cactus convert first — and while any Hugging Face model can in theory go through it, that path is still very experimental, so getting the model you actually want running on-device is far from guaranteed. Its hybrid cloud routing also runs through Cactus's own hosted backend with no self-hosting option, and the hybrid features sit behind a paid tier. Its licence isn't truly open source either: it's source-available with a commercial-use cap, free only for individuals, non-profits, and organisations under $2M in both funding and annual revenue.

Conclusion: NobodyWho is the best AI SDK for on-device inference

Most of the field is either a low-level engine (llama.cpp, ONNX Runtime, MLX) that leaves you to build the app layer yourself, a single-platform solution (MLX, LiteRT's strengths), or a commercial stack tied to a vendor-hosted backend you can't self-host (RunAnywhere, Cactus).

NobodyWho is the one option that puts it all together: a fast, GPU-accelerated runtime on top of the best portable inference engine, a complete on-device stack (LLM + TTS + STT + VAD + multimodal), type-safe tool calling and RAG, use of any GGUF model, and support for every major framework while working fully offline, private by design, and already shipping in real apps on phones, watches, and headsets.

If you're building an AI feature that has to be private, work offline, cost nothing per request, and ship across platforms, NobodyWho is the best AI SDK for on-device inference in 2026.

Get started

• GitHub — star the repo, open an issue, or start a discussion.
• Documentation — pick your framework and ship your first on-device model.
• Discord — ask questions, share what you're building, and chat with the team and other NobodyWho users.

Happy hacking!