Local AI in Early 2026: CES Highlights, New Models, and What It Means for iPhone and Mac

The start of 2026 has brought a wave of exciting developments for local AI. From groundbreaking hardware announcements at CES 2026 to new lightweight models designed for mobile devices, the industry is making it clearer than ever: powerful AI no longer requires the cloud. Here’s what happened in December 2025 and January 2026—and what it means for privacy-focused users on iPhone and Mac.

CES 2026: Hardware That Runs AI Locally

CES 2026 was dominated by on-device AI announcements. The major chip makers and device manufacturers are betting big on local processing.

Intel Core Ultra 300 (Panther Lake)

Intel introduced its Core Ultra 300 series, built on the cutting-edge 18A (2nm) process. These chips are specifically designed to run large AI models directly on laptops without relying on cloud services. The focus is on faster, more secure AI that works offline—a significant step for professionals who need AI capabilities but can’t send sensitive data to external servers.

AMD Ryzen AI 10000 Series

AMD’s response came with the Ryzen AI 10000 series, featuring enhanced AI processing capabilities for tasks like generative video editing and real-time translation. These processors maintain energy efficiency while delivering the compute power needed for local LLM inference, making them ideal for creative professionals and developers.

Compact AI Powerhouses

Two standout devices demonstrated just how far local AI hardware has come:

GMKtec EVO-T2: This mini PC, powered by Intel’s Core Ultra X9 388H, delivers a remarkable 180 TOPS (trillion operations per second) of AI performance—50% more than competing AMD solutions. It’s a desktop-class AI workstation in a compact form factor.
Tiiny AI Pocket Lab: Perhaps the most impressive announcement, this compact device achieved a Guinness World Record by running 120 billion parameter models entirely on-device. With 80GB of RAM, a 12-core ARMv9.2 CPU, and a custom NPU delivering 190 TOPS, it proves that cloud-free AI at scale is not just possible—it’s here.

Multi-Device AI: Lenovo Qira

Lenovo unveiled Qira at CES 2026, describing it as a “Personal Ambient Intelligence” layer. Unlike traditional AI assistants, Qira operates seamlessly across Lenovo laptops, Motorola phones, and wearables like Motorola’s Project Maxwell.

What makes Qira interesting for privacy advocates:

Local-first processing: Most data processing happens on-device rather than in the cloud
User data control: Transparency about what data is used and where it goes
Cross-device coordination: The AI learns your habits and coordinates tasks across your devices
Third-party integration: Works with external AI models like ChatGPT and Gemini when you explicitly choose to use them

This approach—local by default, cloud when you choose—mirrors the philosophy we’ve built into Enclave AI.

New Models for Mobile and Edge Devices

December 2025 brought several model releases optimized for local deployment on resource-constrained devices.

Google FunctionGemma

Google open-sourced FunctionGemma, a 270-million-parameter model designed to translate natural language commands into executable actions—entirely on-device. Running on smartphones, browsers, and IoT devices, FunctionGemma enables voice and text commands to trigger local actions without any data leaving your device. This is particularly exciting for automation and smart home applications.

Qwen 3 Mobile Variants

Alibaba’s Qwen 3 family expanded with models specifically optimized for mobile:

Qwen3-0.6B: A 600-million-parameter model perfect for lightweight tasks on phones and edge devices
Qwen3-1.7B: Ideal for chatbots and low-latency applications on mobile platforms
Qwen3-30B-A3B: A larger model with only 3B active parameters per token, designed to run in real-time even on devices like Raspberry Pi

These models demonstrate that useful AI doesn’t require massive compute—smart architecture and efficient training can deliver impressive results on modest hardware.

Continued Progress in Open-Source

The broader open-source ecosystem continues to mature:

Gemma 3: Google’s open model with context lengths up to 128K tokens and quantization-aware training
Llama 3.1: Meta’s latest, available in 8B, 70B, and 405B parameter sizes
Phi-4: Microsoft’s efficiency-focused models that punch above their weight class

Framework and Research Advances

Beyond models and hardware, the infrastructure for local AI is improving rapidly.

HeteroLLM for Mobile

Research into mobile-optimized inference continues with HeteroLLM, a framework that leverages the heterogeneous accelerators within mobile System-on-Chips. By intelligently partitioning work across CPU, GPU, and NPU, HeteroLLM achieves significant performance improvements over existing mobile inference engines.

Apple’s Developer Tools

For Apple developers, the tooling ecosystem is maturing:

MLX: Apple’s open-source framework for efficient LLM inference on Apple Silicon
AnyLanguageModel: A Swift package providing a unified API for integrating both local and remote LLMs into apps

These tools make it easier than ever to build privacy-respecting AI features into macOS and iOS applications.

What This Means for iPhone and Mac Users

These developments translate directly to better experiences for Apple device users:

For iPhone (and iPad)

The new mobile-optimized models (Qwen3-0.6B, Qwen3-1.7B, FunctionGemma) run comfortably on modern iPhones
A17/A18/A19 chips with Neural Engine provide the hardware foundation
Expect snappier responses and lower battery consumption as inference efficiency improves

For Mac

M-series Macs continue to benefit from unified memory architecture
70B+ parameter models are now practical on high-RAM configurations (64GB+)
The software stack (MLX, llama.cpp, Ollama) is mature and stable

Practical Recommendations

Device	Sweet Spot	Notes
iPhone	0.5B–4B parameters	Qwen3-1.7B, Phi-4 mini, Gemma 3n
Mac (8–16GB)	7B @ Q4/Q5	General chat, writing, light coding
Mac (32–64GB)	14B–32B @ Q4/Q5	Reasoning, multi-file coding
Mac (128GB+)	70B @ Q4	Near-frontier local performance

Experience Local AI with Enclave

At Enclave AI, we’ve been building toward this future since the beginning. Our app brings the latest efficient models to your iPhone and Mac, with:

Complete privacy: Your conversations never leave your device
Offline capability: Works anywhere, no internet required
Regular model updates: We add new efficient models as they become available
Simple setup: No configuration needed—just download and chat

The announcements from CES 2026 and the ongoing model releases confirm what we’ve believed all along: the future of AI is local, private, and powerful.

Download Enclave AI and experience what on-device AI can do for you.

Five Best Local LLMs for iPhone and Mac
See: /blog/2025/07/06/five-best-local-llms-iphone-mac-july-2025/
Local LLMs in September 2025: What’s New
See: /blog/2025/09/06/latest-advancements-local-llms-september-2025/
The Practical Quantization Guide for iPhone and Mac
See: /blog/2025/11/12/practical-quantization-guide-iphone-mac-gguf/