Quickly understand trade-offs across performance, memory, and model size before selecting the right model for your exact needs.
Find optimized models, deployment-ready code, workflows, and optimization tools to build and deploy AI applications on Arm, from cloud to edge.
AI Portal helps you and your coding agents find, evaluate, and optimize AI models, so you can focus on building applications. Optimize your own models for Arm, or use the Arm MCP Server to access AI Portal resources from the tools you already use. Explore the AI Portal Story or the Arm Community blog to learn more.
Pre-optimized models
with performance data
Deployment-ready code
with guided workflows
Optimization tools
to tune your own models
Optimization work already applied so you can spend less time tuning and start building faster.
Start building with code examples, reference applications, deployment scripts, and workflows for Arm-based targets. Use these resources to deploy AI models on Arm with less integration work and a clearer path from model selection to a running application.
Explore Code ExamplesBrowse Learning PathsOptimize your own models, including proprietary models, and get early access to upcoming Arm optimization tools. Analyze performance, apply techniques such as quantization and model conversion, tune for your target hardware, and evaluate results as you work toward deployment.
Connect your coding agent to AI Portal through the Arm MCP Server to discover models and workflows, compare options, and find resources for your target hardware without leaving your development environment.
Run the following command in your terminal:
codex mcp add arm-ai --url https://mcp.api.devplatform.arm.com/ai-portal
Copied
Run the following command in your terminal:
claude mcp add --transport http arm-ai https://mcp.api.devplatform.arm.com/ai-portal
Copied
Run the following command in your terminal:
copilot mcp add --transport http arm-ai https://mcp.api.devplatform.arm.com/ai-portal
Copied
Add the MCP URL to your client, and set Transport: Streamable HTTP
https://mcp.api.devplatform.arm.com/ai-portal
Copied
Run the following command in your terminal:
qoder mcp add arm-ai -t http https://mcp.api.devplatform.arm.com/ai-portal
Copied
Run the following command in your terminal:
trae --add-mcp '{"name":"arm-ai","type":"http","url":"https://mcp.api.devplatform.arm.com/ai-portal"}'
Copied
Run the following command in your terminal:
codebuddy mcp add --transport http arm-ai https://mcp.api.devplatform.arm.com/ai-portal
Copied
Add the MCP URL to your client, and set Transport: Streamable HTTP
https://mcp.api.devplatform.arm.com/ai-portal
Copied
Quickly understand trade-offs across performance, memory, and model size before selecting the right model for your exact needs.
Find models optimized for supported Arm hardware across cloud, edge, embedded, and mobile.
Target Linux-based edge AI across automotive, robotics, IoT, and other intelligent systems.
Run on-device AI on embedded NPUs designed for constrained devices and dedicated acceleration.
Build neural graphics and AI-enhanced visual experiences for supported Arm-based mobile GPUs.
Find models for language, speech, vision, and other AI workloads running on mobile CPUs.
Run AI inference on Arm-based cloud CPUs with models optimized for supported server environments.
Discover Arm-optimized models and deployment resources from across the AI ecosystem, with options for mobile, edge, and other Arm-based targets.
Arm AI Portal helps developers and coding agents find, evaluate, optimize, and deploy AI models on Arm. Access pre-optimized models, performance data, deployment-ready code and workflows, optimization tools, and agent-ready resources across cloud, edge, and physical AI. Get started with the AI Portal Learning Path.
Find AI models by task, use case, or target Arm hardware. Explore pre-optimized models for generative AI, computer vision, audio and natural language processing, and neural graphics. Review available performance and accuracy data to help you choose the right model for your application.
Yes. Use the AI Portal comparison tool to compare supported models for your task and target device class. Review performance, memory use, and model size to understand trade-offs and make a more informed model selection for your Arm hardware.
Use deployment-ready code examples, reference applications, deployment scripts, and guided Learning Paths to deploy AI models on Arm. Follow these resources to move from model selection and evaluation to a running application on your target Arm platform.
Yes. Connect supported coding agents to AI Portal through the Arm MCP Server to access relevant models and workflows from your development environment. Supported agentic development environments include Claude Code, Codex, GitHub Copilot, Qoder, TRAE, and CodeBuddy.
Yes. Bring your own AI models, including proprietary models, and optimize them for supported Arm hardware through the Early Access Program. Upcoming optimization tools support performance analysis, quantization, and model conversion.
AI Portal provides optimized models for supported Arm-based compute across cloud, edge, and physical AI. Browse models for Cloud CPU, Mobile CPU, Mobile GPU, Embedded Linux, and Embedded NPU targets to find resources relevant to your deployment hardware.
AI Portal supports models and resources across multiple AI runtimes and developer tools. Current resources include models for ExecuTorch, LiteRT, and ONNX Runtime. You can also access Arm-optimized models through Hugging Face and agentic access through the Arm MCP Server.
Find deployment-ready code examples, reference applications, deployment scripts, and workflows in the AI Portal Code section. Use these resources to reduce integration work and move more quickly from model selection to deployment on supported Arm targets.