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

Statistical Methods for Evaluating LLM Performance

The large language model (LLM) has become a cornerstone of many AI applications.

2 hours ago

Getting started with computer use in Amazon Bedrock Agents

Computer use is a breakthrough capability from Anthropic that allows foundation models (FMs) to visually…

2 hours ago

OpenAI’s strategic gambit: The Agents SDK and why it changes everything for enterprise AI

OpenAI's new API and Agents SDK consolidate a previously fragmented complex ecosystem into a unified,…

3 hours ago

Under Trump, AI Scientists Are Told to Remove ‘Ideological Bias’ From Powerful Models

A directive from the National Institute of Standards and Technology eliminates mention of “AI safety”…

3 hours ago

Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models

Speech foundation models, such as HuBERT and its variants, are pre-trained on large amounts of…

1 day ago

How GoDaddy built a category generation system at scale with batch inference for Amazon Bedrock

This post was co-written with Vishal Singh, Data Engineering Leader at Data & Analytics team…

1 day ago