Categories: FAANG

On Device Llama 3.1 with Core ML

Many app developers are interested in building on device experiences that integrate increasingly capable large language models (LLMs). Running these models locally on Apple silicon enables developers to leverage the capabilities of the user’s device for cost-effective inference, without sending data to and from third party servers, which also helps protect user privacy. In order to do this, the models must be carefully optimized to effectively utilize the available system resources, because LLMs often have high demands for both memory and processing power.
This technical post details how to…
AI Generated Robotic Content

Recent Posts

AI, Light, and Shadow: Jasper’s New Research Powers More Realistic Imagery

Jasper Research Lab’s new shadow generation research and model enable brands to create more photorealistic…

22 hours ago

Gemini 2.0 is now available to everyone

We’re announcing new updates to Gemini 2.0 Flash, plus introducing Gemini 2.0 Flash-Lite and Gemini…

22 hours ago

Reinforcement Learning for Long-Horizon Interactive LLM Agents

Interactive digital agents (IDAs) leverage APIs of stateful digital environments to perform tasks in response…

22 hours ago

Trellix lowers cost, increases speed, and adds delivery flexibility with cost-effective and performant Amazon Nova Micro and Amazon Nova Lite models

This post is co-written with Martin Holste from Trellix.  Security teams are dealing with an…

22 hours ago

Designing sustainable AI: A deep dive into TPU efficiency and lifecycle emissions

As AI continues to unlock new opportunities for business growth and societal benefits, we’re working…

22 hours ago

NOAA Employees Told to Pause Work With ‘Foreign Nationals’

An internal email obtained by WIRED shows that NOAA workers received orders to pause “ALL…

23 hours ago