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Arm and PyTorch: Accelerating AI from Cloud to Edge

The collaboration between Arm and Meta empowers AI developers to build and deploy high-performance, energy-efficient generative AI (GenAI) applications across cloud, mobile, and edge using PyTorch and ExecuTorch.

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Conference

Arm at Pytorch Conference Europe 2026

April 7-8 | Paris, France

Join us onsite to find out how Arm gives you the flexibility, performance, and efficiency you need across the AI lifecycle. Expect insightful talks and poster presentations.

Tuesday, April 7

Talk: Edge-native AI agents with MCP

Parichay Das, Associate Principal, LTM Limited and Arm Distinguished Ambassador

10:55 – 11:05 a.m. CEST
Junior Stage

See how MCP, ExecuTorch, and Arm hardware combine to unlock production-ready, interoperable agentic AI at the edge.

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Talk: Accelerating on-device ML inference with ExecuTorch and Arm SME2

Jason Zhu, Senior Principal Engineer, Arm

2:05 – 2:15 p.m CEST
Master Stage

Learn how ExecuTorch and Arm Scalable Matrix Extension 2 (SME2) enable efficient CPU deployments of production AI workloads.

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Poster: On-CPU inference and TorchInductor optimizations

Aditya Tewari, Senior Software Engineer, Arm

5:00 – 6:30 p.m. CEST
Open Platform

Hear about CPU-focused optimizations in PyTorch to enhance TorchInductor performance, spanning BF16 to INT8 and INT4.

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Wednesday, April 8

Talk: Optimizing CPU LLM inference in PyTorch: Lessons from vLLM

Crefeda Rodrigues, Staff Software Engineer, Arm

1:35 – 2:00 p.m. CEST
Founders Cafe

Explore learnings from building and upstreaming a high-performance CPU inference engine using vLLM.

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Talk: Running ExecuTorch applications with silicon acceleration in ultra-low power

George Gekov, ML Engineer, Arm Aki Makkonen, Senior Staff Application Engineer, Alif Semiconductor

2:05 pm –2:15 p.m. CEST
Master Stage

Discover how to build an end-to-end speech recognition application using ExecuTorch on the Arm-based Alif Ensemble E8 SoC.

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Get Started

Get Started with PyTorch on Arm

Dive into the code repositories, model collections, and performance libraries to help you train, optimize, and deploy PyTorch on Arm today.

PyTorch on GitHub

Train and deploy AI models with PyTorch, optimized for high-performance on Arm from cloud to edge.

Access PyTorch repo

ExecuTorch on GitHub

Build efficient AI applications for Arm-based edge and mobile devices using Meta’s lightweight runtime.

Access ExecuTorch repo

Arm Kleidi Libraries

Arm Kleidi integrates with the most popular AI frameworks to enable optimum performance on Arm-based platforms out of the box.

Discover Arm Kleidi

Hugging Face Model Collections

Discover AI models you can deploy on Arm-based devices or cloud platforms, including those built with PyTorch and ExecuTorch.

Explore model collections

Cloud Resources

Resources for PyTorch on Server and Cloud

LEARNING PATHS
CODE-ALONGS & VIDEOS
BLOGS

Build and scale AI with PyTorch on Arm cloud

Visit our Developer Launchpad to learn how to deploy on Arm.

LLM chatbot with PyTorch on Arm servers

Quantize and run a Llama 3.1- based chatbot on Arm.

Run an NLP model from Hugging Face

Run PyTorch NLP models on Arm-based instances.

Train PyTorch models for digit classification

Train a feedforward neural network (FNN) and apply optimization.

Deploy a vision chatbot on Google Axion processors

Deploy a production-ready vision chatbot.

Measure and accelerate inference on Arm servers

Evaluate the performance of NLP and vision models.

Optimize MLOps with Arm-hosted GitHub runners

Automate training and testing steps in an ML workflow.

Use Keras Core with multiple backends

Use the Keras Core library with TensorFlow, PyTorch, and JAX.

Run Llama with PyTorch on Arm-based infrastructure

Create a browser-based LLM app that runs on Arm CPUs.

Accelerating LLMs on Arm-based Graviton processors

See how reducing memory footprint boosts performance.

GenAI with AWS Graviton processors | AWS OnAir

Learn when to use AWS Graviton for AI/ML workloads.

Accelerating DLRMv2 inference on Arm Neoverse

Accelerate recommendation model inference in the cloud.

AI performance boost with Arm Kleidi | PyTorch Conference 2024

How Kleidi enhances performance on AWS Graviton.

Streamlining MLOps with GitHub Arm64 runners

Unlock new levels of efficiency and performance in your ML projects with GitHub on Arm.

Accelerated PyTorch inference with torch.compile

Use optimized torch.compile performance to improve PyTorch model inference.

Mobile Resources

Resources for Building Mobile AI Applications

LEARNING PATHS
CODE-ALONGS & VIDEOS
BLOGS

Fine-tuning neural graphics models with model gym

Train and deploy upscaling models for neural super sampling (NSS).

Build an Android chat app with ExecuTorch and Arm Kleidi

Build and run apps with Llama models using ExecuTorch on an Arm-based smartphone.

Build an Android chat app with Llama and Arm KleidiAI

Learn how to build and deploy a ChatAI application for Android mobile devices.

Edge AI acceleration demo | PyTorch Conference 2024

Learn how Arm KleidiAI, integrated with ExecuTorch, accelerates AI on mobile.

Faster GenAI at the edge with Arm and ExecuTorch

See how the Arm and ExecuTorch collaboration is improving edge AI performance.

ExecuTorch 1.0 is here with SME2 optimizations

With SME2 support via Arm KleidiAI, run ExecuTorch models natively on-device.

Arm neural technology in ExecuTorch 1.0

The ExecuTorch GA release includes backend support for Arm neural technology.

Bring GenAI to the masses with ExecuTorch and Arm KleidiAI

ExecuTorch 0.7 enables KleidiAI to automatically accelerate AI performance.

Interactive Image Segmentation Using SqueezeSAM

How SME2 enables up to 3.9x inference speedup for the Instagram Cutouts tool using Arm Kleidi, XNNPACK, ExecuTorch, and SqueezeSAM.

Automatic Speech Recognition with Conformer Models

Run high-quality speech and audio models at the edge using ExecuTorch and Arm Ethos-U85.

Edge Resources

Resources for Deploying Models at the Edge

LEARNING PATHS
VIDEOS
BLOGS

Run Llama 3 on a Raspberry Pi 5 using ExecuTorch

Learn techniques for running large language models (LLMs) in an embedded environment.

Introduction to TinyML on Arm using PyTorch and ExecuTorch

Set up and configure a TinyML development environment using Arm Corstone-320 FVP.

PyTorch and ExecuTorch rock-paper-scissors

Deploy TinyML models on Arm-based edge devices using PyTorch and ExecuTorch.

Visualize Arm Ethos-U NPU performance on Arm FVPs

Deploy TinyML models and visualize execution using the FVP graphical interface.

Bring PyTorch models to Arm Cortex-M | Arm AI tech talk

Develop a deep learning model with PyTorch and deploy it on Cortex-M-based systems.

How Arm Ethos-U and ExecuTorch 1.0 powers edge AI

ExecuTorch 1.0 brings PyTorch performance and portability to the most efficient Arm devices.

Deploying PyTorch models on Arm edge devices

Tutorial for deploying PyTorch on devices like Raspberry Pi or NVIDIA Jetson Nano.

Get started with PyTorch, ExecuTorch, and Ethos-U85

Check out the code and tools for seamlessly deploying AI models on Arm solutions.

Arm at PyTorch Conference 2025: Power-Efficient AI Everywhere

Arm demonstrated how to achieve the flexibility, performance, and efficiency you need across the AI lifecycle through insightful talks, hands-on demos, and one-on-one workshops.

Architecting AI: Efficient Models, Everywhere

Sharbani Roy, VP of AI Services, Arm

Discover how Arm enables developers to harness small language models (SLMs) and vision models, optimize inference at scale, and build sustainable AI systems.

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Deploying GenAI for Audio Gen on Mobile CPUs with ExecuTorch

Gian Marco Iodice, GenAI Engineering Lead, Arm

Explore practical use cases of GenAI running on device and gain insight into integration workflows, optimizations, and efficient deployment.

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Join the Arm Developer Program

Build AI at scale on Arm with a personalized dashboard, early access tools, on-demand labs, courses and certifications, badging, and direct access to Arm experts.

Explore the Program

PyTorch News

Redefining Edge AI with ExecuTorch 1.0 GA Release

Seamlessly deploy PyTorch models across billions of Arm-based edge devices, unlocking faster on-device AI.

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Key Enhancements in the PyTorch 2.9 Release

Learn about the key enhancements Arm has contributed to the latest PyTorch release.

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PyTorch Growth: 2024 Year in Review

PyTorch has become the dominant open source deep learning framework for AI and ML with a 63% adoption rate.

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ExecuTorch Beta: On-Device AI

Deploy LLMs at the edge with ExecuTorch, optimized for Arm-based devices.

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GenAI at the Edge on Arm with ExecuTorch Beta

Integrate new quantized models into your applications for faster AI at the edge.

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Arm Joins the PyTorch Foundation

PyTorch Foundation welcomes Arm as a premier member and part of the PyTorch Foundation Governing Board.

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