Categories: FAANG

Updates to Apple’s On-Device and Server Foundation Language Models

With Apple Intelligence, we’re integrating powerful generative AI right into the apps and experiences people use every day, all while protecting their privacy. At the 2025 Worldwide Developers Conference we introduced a new generation of language foundation models specifically developed to enhance the Apple Intelligence features in our latest software releases. We also introduced the new Foundation Models framework, which gives app developers direct access to the on-device foundation language model at the core of Apple Intelligence.
We crafted these generative models to power the wide range of…

Apple Intelligence Foundation Language Models Tech Report 2025

We introduce two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and services: (i) a ∼3B-parameter on-device model optimized for Apple silicon through architectural innovations such as KV-cache sharing and 2-bit quantization-aware training; and (ii) a scalable server model built on a novel Parallel-Track Mixture-of-Experts…

July 18, 2025

In "FAANG"

Apple Intelligence Foundation Language Models

We present foundation language models developed to power Apple Intelligence features, including a ∼3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This…

July 30, 2024

In "FAANG"

Lightweight Champ: NVIDIA Releases Small Language Model With State-of-the-Art Accuracy

Developers of generative AI typically face a tradeoff between model size and accuracy. But a new language model released by NVIDIA delivers the best of both, providing state-of-the-art accuracy in a compact form factor. Mistral-NeMo-Minitron 8B — a miniaturized version of the open Mistral NeMo 12B model released by Mistral…

August 22, 2024

In "FAANG"

AI Generated Robotic Content