Summary
The video delves into the challenges and solutions for running large L models on edge devices like mobile phones and Raspberry Pi. It covers aspects such as calculating GPU needs, VRAM consumption, and considerations for storage and activation memory. Additionally, it provides guidance on deploying models on Android and iOS devices, with a focus on optimizing VRAM needs and leveraging MLX for local inference. The content also includes steps for building AI apps on iOS using MLX, which involves adding packages, interacting with large L models, and integrating Swift UI for user engagement.
Introduction to Running Large L Models on Edge Devices
The video introduces the concept of running large L models on edge devices such as mobile phones and Raspberry Pi, enabling the deployment of state-of-the-art models on smart hardware directly.
Challenges of Running Large L Models
Discussing the challenges of running large L models, including the variable costs associated with usage and the need to generate revenue to cover costs.
Calculating Hardware Requirements for Local Model Execution
Explaining the process of calculating GPU needs and VRAM consumption for running local models on mobile devices, including considerations for storage and activation memory.
Deploying Models on Android and iOS
Guidance on deploying models on Android and iOS devices, including choosing the right model, estimating VRAM needs, and utilizing platforms like MLX to enable local inference.
Building AI Apps on iOS with MLX
Detailing the steps to build AI apps on iOS using MLX, including adding packages, creating conversations with large L models, and integrating Swift UI for user interaction.
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