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If you’ve ever wondered which GGUF quantization to choose—Q4_K_M, Q5_K_M, or Q8_0—this guide gives you practical, device-specific advice for iPhone and Mac. We’ll keep it simple: what the names mean, how they impact memory, speed, and quality, and clear presets to get great results in Enclave.

Local large language models took another leap this summer. Beyond faster chips, we saw real progress in platform support, in‑browser inference, friendlier tools, and model efficiency. Here’s what changed—and how to take advantage of it on iPhone and Mac.

Choosing the “right size” model has changed a lot since last year. In 2025, training recipes, data quality, and smarter architectures often matter more than raw parameter count. This guide updates our most-read piece with the latest facts, practical device picks, and a simple decision flow you can use today.

The AI world was rocked this week when OpenAI officially announced their most surprising move yet: GPT-OSS, a family of powerful open source AI models that marks the company’s dramatic return to its open roots. After years of keeping their most advanced models behind closed doors, OpenAI has revealed what could be the most significant open source AI breakthrough since Meta’s Llama series.

Just when the AI world seemed to have settled into a rhythm of bigger and bigger transformer models, Liquid AI has once again shaken things up. Following the success of their first-generation models, the company has just unveiled the LFM-2 series—a new family of Liquid Foundation Models that represents a major leap forward in efficient, powerful, and private AI.

The landscape of local AI has transformed dramatically in 2025, with powerful language models now running seamlessly on iPhones and Macs. From Apple’s latest neural engines to the unified memory architecture of Apple Silicon, our devices have become capable AI powerhouses that rival cloud-based solutions while keeping your data completely private.

The next wave of on-device artificial intelligence has arrived. We are incredibly excited to announce that Google’s revolutionary Gemma 3n model is now fully integrated into Enclave AI for both iOS and macOS.

We are incredibly excited to announce a groundbreaking new feature for Enclave AI on iOS: Direct Hugging Face GGUF Model Search and Integration! This powerful update empowers you to explore and utilize a virtually limitless library of open-source GGUF models, all while maintaining Enclave AI’s signature on-device processing and complete privacy.

We are thrilled to announce the integration of the cutting-edge Qwen3 family of large language models into Enclave AI. This update brings powerful new capabilities, including innovative Mixture-of-Experts (MoE) architectures and unique hybrid thinking modes, directly to your device while upholding our core commitment to absolute privacy.

We’re excited to announce that Enclave AI now supports Google’s groundbreaking Gemma 3 family of models on iOS, with full macOS support coming soon. This integration brings unprecedented capabilities to your device while maintaining our unwavering commitment to privacy and offline processing.

We’re excited to announce a major enhancement to Enclave AI’s integration with Apple Shortcuts: the ability to select specific AI models for your automations. This powerful update, now available on both iOS and macOS, gives you unprecedented control over your AI-powered workflows.

We’re thrilled to announce a highly requested feature that enhances both productivity and privacy: local conversation history in Enclave AI. Starting today, you can access, review, and continue your previous conversations—all while maintaining our unwavering commitment to privacy through complete local storage.

We’re excited to announce a groundbreaking update to Enclave AI: seamless integration with OpenRouter. This new feature gives you unprecedented flexibility to choose between completely private local models and powerful cloud-based AI options—all through the same familiar interface you already know and love.

The future of AI is not just powerful—it’s portable. Today, we’re thrilled to announce that Enclave AI now supports DeepSeek R1’s distilled models, bringing advanced reasoning and problem-solving capabilities right to your iOS device while maintaining complete privacy.

We’re excited to announce that Enclave iOS users can now download and use additional top-tier open source language models, all optimized specifically for mobile devices. This expansion brings more choice and flexibility to your private AI experience.

We’re thrilled to announce that DeepSeek R1, a groundbreaking open source AI model, will soon be available in Enclave AI for both iOS and macOS. This addition represents a significant advancement in local AI capabilities, particularly for users who need strong reasoning, mathematics, and coding abilities while maintaining complete privacy.

We’re excited to announce a transformative new feature in Enclave AI that’s changing how users interact with their documents. Now you can add PDF and text documents directly into your chat context, enabling rich, contextual conversations about your content while maintaining complete privacy through local processing. This enhancement opens up new possibilities for document analysis, research, and professional work - all while keeping your sensitive information secure on your device.

As artificial intelligence becomes increasingly integral to professional workflows, the need for privacy-preserving AI solutions has never been greater. Local AI, running entirely on-device without cloud connectivity, is emerging as a game-changer for industries handling sensitive information. Let’s explore how offline AI is transforming various professional sectors while maintaining strict data privacy standards.

Microsoft recently unveiled its newest generative AI model, Phi-4, marking a notable step forward in the evolution of the Phi series. Designed to address complex mathematical challenges, this model introduces several advancements attributed largely to the improved quality of its training data. Microsoft credits the combination of high-quality synthetic data and human-generated content for its enhanced capabilities, alongside other undisclosed refinements made during intermediate training stages.

As we bid farewell to 2024 and welcome 2025, we’re standing at the threshold of an exciting new era in artificial intelligence. This past year has seen remarkable advances in local AI technology, with open source models becoming more powerful and efficient than ever before. Let’s celebrate these achievements and look forward to what 2025 holds for private, offline AI.

As we approach 2025, the open source AI landscape is evolving rapidly. The coming year promises significant advances in local, private AI technology that will reshape how we interact with artificial intelligence. Let’s explore the key developments we expect to see.

As the holiday season approaches, many of us are looking for meaningful gifts that combine innovation with practicality. In a world where digital privacy becomes increasingly precious, what could be more thoughtful than the gift of truly private AI assistance?

Remember when using AI meant creating yet another online account, sharing your email, and agreeing to lengthy privacy policies? Those days are rapidly becoming history. A new wave of AI assistants is emerging, championing anonymity and privacy without sacrificing capability. This shift represents more than just a trend – it’s a fundamental change in how we interact with artificial intelligence.

As local AI technology continues to evolve rapidly, it’s important to separate reality from hype. Let’s explore what’s actually possible with local AI today, and what realistic developments we can expect in the near future.

When we started building Enclave AI, we had a clear vision: create an AI assistant that’s both powerful and completely private. Today, we want to share our journey in building a local AI solution that puts user privacy first, and the important decisions that shaped our development process.

The landscape of artificial intelligence is being transformed by open source models. While tech giants once dominated AI development, community-driven projects are now leading the charge toward more accessible, transparent, and private AI solutions. Let’s explore how open source models are revolutionizing the way we interact with AI.

The power of artificial intelligence doesn’t have to come with privacy concerns or subscription fees. Local AI running on your own device opens up a world of creative possibilities while keeping your data private. Here are five innovative ways to incorporate offline AI into your daily life, all without sending your data to the cloud.

With the rising costs of AI services like ChatGPT Plus ($20/month) and Claude Pro ($20/month), many users are looking for alternatives. Let’s break down the real costs of local AI versus cloud services and see why running AI locally might be the smarter financial choice.

In an age where data is often called “the new oil,” protecting your privacy has never been more important. Every day, millions of people share their thoughts, ideas, and personal information with AI chatbots - but where does all that data go?

When getting started with local AI, one of the most important decisions is choosing the right model size. Bigger isn’t always better - it’s about finding the sweet spot between performance and practicality for your specific needs.

If you’ve been curious about AI but concerned about privacy, you’re not alone. While ChatGPT and similar services have shown the power of AI, they require sending your data to remote servers. But there’s another way: running AI models locally on your own device. Let’s explore what that means and how to get started.

Discover how to create personalized AI assistants using local language models, ensuring complete privacy and offline functionality. Unlike cloud-based assistants, these local AI personalities keep your data private while providing powerful assistance.

Combine the power of local language models with Apple Shortcuts to create private, offline automation workflows that transform your productivity. Unlike cloud-based AI solutions, our local LLM approach ensures your automation stays completely private and works anywhere.

In an era where data breaches make headlines daily and privacy concerns are mounting, local AI and offline language models aren’t just features—they’re necessities. Let’s explore why running AI models locally on your device represents the future of private computing.

In today’s digital world, privacy is more important than ever. Every click, every conversation, and every interaction online leaves a trace—but what if you didn’t have to sacrifice your privacy to use cutting-edge technology?