On-Device AI Frameworks Compared: 2026 Decision Guide

Q: What is the best on-device AI framework in 2026?

There is no single best framework, and any guide that names one is really recommending its own defaults. The right choice is determined by three constraints you must fix before looking at any benchmark. First, is the model an LLM or a general model such as vision, audio, or classification — llama.cpp and MLC LLM are LLM runtimes, while ONNX Runtime, LiteRT, and Core ML are general-purpose. Second, do you ship to Apple only or to Android and the web as well — Core ML is Apple-only, whereas the others are cross-platform to varying degrees. Third, can you tolerate an ahead-of-time compile step in exchange for speed and reach, which is the MLC trade, versus llama.cpp point-at-a-file simplicity. Once those three answers are written down, the field of five collapses to one or two real candidates, and the tokens-per-second numbers that dominate blog comparisons become a tiebreaker rather than the decision. The most common and most expensive mistake is choosing a framework because a benchmark post praised its speed, then discovering it does not support your model class or strands your second platform. Match the framework to the constraints, not to the leaderboard.

Q: When should I use llama.cpp versus MLC LLM for on-device inference?

Both are LLM runtimes, so the choice between them is about deployment targets and your iteration loop rather than model support. llama.cpp is the portable workhorse: it consumes GGUF files, needs no ahead-of-time compilation of the model, and runs essentially anywhere C compiles, using CPU first with GPU acceleration through Metal, CUDA, and Vulkan. That file-and-go model is ideal when you iterate on models frequently, want a single C or C++ dependency you control, and are shipping to native platforms. MLC LLM takes a different path: it compiles the model ahead of time through the TVM compiler stack, which is how it reaches targets llama.cpp does not easily hit, most notably WebGPU in the browser, and how it squeezes specific hardware. The cost of that compilation is friction in your development loop and an extra step in your build pipeline, which compounds if you change models weekly and is negligible if you ship one model and rarely touch it. The clean decision rule: if you need the browser or want a single source compiled to many native targets, choose MLC; if you want portability, control, and fast iteration on native platforms, choose llama.cpp. Neither is faster in the abstract — they optimise for different deployment realities.

Q: What is the difference between ONNX Runtime, LiteRT, and Core ML?

All three are general-model runtimes — meaning they run vision, audio, NLP, and increasingly LLM workloads rather than being LLM-only — but they differ sharply on platform reach and accelerator access. ONNX Runtime is the cross-platform generalist: it runs the ONNX format across iOS, Android, desktop, and cloud, reaching accelerators through Execution Providers such as NNAPI, the Core ML EP, QNN, and XNNPACK, which makes it the natural choice when you want one runtime and one model format spanning mobile and server, or when your fleet mixes many model types. LiteRT, the framework formerly called TensorFlow Lite, is Android-first with the deepest NPU story through its GPU, NNAPI, and QNN delegates, and it now also runs on-device LLMs via LiteRT-LM; it is the strongest option when Android is primary and you need dedicated neural-accelerator access. Core ML is Apple-only and gives you the tightest integration with the Apple Neural Engine and the operating system, which is the best on-device performance you can get on iPhone and Mac — at the cost of hard platform lock-in through the .mlpackage format. The rule of thumb: mixed fleet across mobile and cloud favours ONNX Runtime; Android-primary with heavy NPU needs favours LiteRT; Apple-only favours Core ML.

Q: Is LiteRT the same as TensorFlow Lite?

Yes. LiteRT is the rebranded name for what was previously TensorFlow Lite, Google on-device inference runtime, and the rename reflects that it has broadened beyond TensorFlow-only models. Practically, if you have existing TensorFlow Lite knowledge, .tflite models, or delegate configurations, that experience transfers directly to LiteRT. The framework is Android-first but also runs on iOS and embedded targets, and its strongest differentiator is accelerator access: through its delegate system it reaches GPU, NNAPI, and dedicated NPUs including via QNN, which is why it tends to have the deepest neural-accelerator story on Android hardware. Google has also layered on-device LLM support through LiteRT-LM, which replaced the earlier MediaPipe LLM path — an important detail, because it signals that the on-device LLM APIs in Google ecosystem have moved and renamed at least once, so you should build against the current LiteRT-LM surface rather than older MediaPipe LLM examples you may find in tutorials. When your product is Android-primary and battery or latency budgets depend on reaching the NPU rather than falling back to CPU or GPU, LiteRT is usually the framework that gets you there most directly.

Q: Why does quantization matter so much for on-device AI?

Because on-device is where the memory budget is brutal and quantization is what decides whether the model runs at all, not merely how fast it runs. A 7-billion-parameter model at full FP16 precision is roughly 14 GB, which does not fit alongside the operating system and your app on a phone; quantized to 4-bit it drops to around 4 GB, and on a mid-range device the practical budget is often under 2 GB, which pushes you toward smaller models or more aggressive quantization still. So unlike the cloud, where quantization is a cost-and-throughput optimisation you can defer, on-device it is a gating constraint you must design around from the start. This also makes the quantization format a first-class framework-selection criterion rather than an afterthought, because each runtime has its own story: GGUF k-quants in llama.cpp, INT8 in ONNX Runtime, palettization in Core ML, and format-specific schemes in LiteRT. The question is not only which framework is fastest but which one you can produce good-quality quantized weights for, in a format it consumes, at the size your device budget allows. Teams that choose a framework on benchmarks and discover its quantization path does not fit their model or their memory ceiling end up re-doing the decision, which is why quantization belongs at the front of the evaluation, not the end.

Q: Can I ship a production feature on the MediaPipe LLM Inference API?

Be cautious, because Google own documentation describes the MediaPipe LLM Inference API as experimental and intended for research rather than full production deployment, and the on-device LLM space around it has already moved once — LiteRT-LM replaced the earlier MediaPipe LLM path. Building a shipped, revenue-bearing feature on an API the vendor labels research-grade means inheriting its churn: interfaces can change, the API can be renamed or superseded, and examples you built against can stop matching the current surface. That does not make MediaPipe useless — its Tasks APIs for vision and audio are widely used, and prototyping an LLM feature on the experimental API to validate a concept is entirely reasonable. The discipline is to separate prototyping from shipping. For a production on-device LLM on Android, prefer the current LiteRT-LM surface, or a mature cross-platform runtime such as llama.cpp or MLC, and read the vendor maturity language before you commit. The broader lesson applies to the whole fast-moving on-device LLM ecosystem: check whether the specific API you are about to depend on is described as stable or experimental, because the difference is the one that determines whether your next release is a feature update or a forced rewrite.

Q: How do I make sure my framework actually uses the device NPU?

Verify it on your real target hardware, because NPU access differs by framework and by chip, and the datasheet claim that a phone has a neural processor tells you nothing about whether your chosen runtime actually reaches it for your model. The reason the NPU matters is that on-device inference is frequently justified on power and latency grounds, and those budgets usually assume neural-accelerator execution rather than CPU or even GPU fallback; if the framework silently falls back, your battery and latency numbers miss and the whole case for on-device weakens. The paths differ concretely. Core ML reaches the Apple Neural Engine on Apple silicon. LiteRT reaches NPUs on Android through its delegate system including QNN. ONNX Runtime reaches accelerators through Execution Providers such as NNAPI and QNN. llama.cpp and MLC lean more on GPU acceleration through Metal and Vulkan than on the dedicated NPU. Two practical steps prevent the surprise. First, confirm the specific operators in your model are supported on the NPU path for your framework, because an unsupported operator forces a fallback for the whole subgraph. Second, measure on the actual device matrix you ship to, watching for the fallback rather than trusting the configuration, because the one device you did not test is where the leak shows up in production.

Q: Should I use a portability abstraction like ExecuTorch instead of picking one framework?

Treat portability layers as a hedge you pay for, not a free escape from the native decision. ExecuTorch, PyTorch on-device runtime, and cross-runtime formats genuinely reduce lock-in and let you target multiple backends from one source, which is valuable when you know you will ship to several platforms and want to avoid committing to a single vendor format such as Core ML .mlpackage. The cost is real, though: an abstraction layer rarely reaches every accelerator as well as the native path does, and on-device is precisely the domain where a leaky abstraction leaks in production — on the NPU path, on a specific chip, on the one device configuration you did not test. So the honest framing is a trade rather than a strict upgrade. If your priority is minimising rewrite risk across many platforms and you can accept some accelerator inefficiency, a portability layer is a sound choice. If your priority is maximum performance on a known target — the last millisecond of latency or the last hour of battery — the native framework for that platform, Core ML on Apple or LiteRT on Android, will usually beat the abstraction. Many teams use both: a portability layer as the default path and a native fallback for the platforms where performance is a differentiator. The mistake is assuming the abstraction is free; budget for the accelerator gap and test it on real hardware.