---
title: "LLM Inference On-Device in React Native: The Practical Aspects "
description: "A practical look at reliability, performance, libraries, and tradeoffs when running LLM inference locally in React Native apps."
url: https://www.callstack.com/events/llm-inference-on-device-in-react-native-the-practical-aspects
updated: 2026-10-24T08:39:20.861Z
---

# LLM Inference On-Device in React Native: The Practical Aspects 

A practical look at reliability, performance, libraries, and tradeoffs when running LLM inference locally in React Native apps.

_Talk · 1/29/2026 · Recording: 31:09 · Category: React Native_

Organizer: Callstack & Zalando

Speakers: Artur Morys-Magiera (Software Engineer @ Callstack)

[Watch the recording](https://www.youtube.com/watch?v=8U1_RJzoFGE)

Artur Morys-Magiera explored what it actually means to run LLM inference directly on mobile devices in React Native applications. Instead of treating “AI” as a buzzword, he narrowed the focus to LLMs and examined why teams might move inference on-device: reliability without network dependency, stronger privacy guarantees, and lower latency without cloud queues. He also highlighted real user-facing constraints, including model size, disk usage, and hardware variability across iOS and Android devices.

From there, the talk moved into the practical engineering layer: hardware acceleration (GPU, NPU, CPU), runtime fragmentation, debugging challenges with abstraction layers like OpenCL, and real-world performance issues traced to memory layout differences. Artur compared available libraries, explained their tradeoffs, and showed how a unified API approach can simplify integration while still supporting optimizations such as quantization, compilation-time improvements, and model selection based on device capability.

#### What you’ll walk away with:

- When running LLMs locally improves reliability, privacy, and latency
- How model size and OS-provisioned models impact real user experience
- Why hardware acceleration and device fragmentation shape performance decisions
- The tradeoffs between TF Lite, ONNX, ExecuTorch, MLC, llama.cpp, and Apple-based solutions
- How quantization, compilation optimizations, and unified APIs reduce integration risk