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Backburner splits a 27-billion-parameter language model between a MacBook Pro and iPhone connected via USB-C, achieving 29-44% faster inference on long contexts by distributing computation across both devices' GPUs. The system maintains identical outputs to running the model on the Mac alone while supporting up to 196k tokens of context on an iPhone 17 Pro Max.
- Split prefill workload: Mac runs layers 1-40 while iPhone runs layers 41-64 in parallel, with the iPhone holding older KV cache pages past 64k context and computing attention over them during both prefill and decoding.
- Measured performance gains: 44% faster throughput at 16k context (157 vs 109 tok/s), and ability to maintain 8-bit precision at 128k tokens versus the Mac alone dropping to 4-bit to fit in memory.
- Greedy decoding is token-identical between Mac-only and Mac+iPhone configurations across tested context lengths (8k, 32k, 140k), confirming the split computation produces correct results.
VirtualBuddy runs macOS 12+ and ARM-based Linux virtual machines on Apple Silicon, automatically downloading or using custom IPSW/ISO images. It offers APFS-based snapshots, file and clipboard sharing, recovery mode, and beta device support via Xcode packages. You can customize hardware, save VM states, and build the app from source with Xcode 16.
- VirtualBuddy lets you run macOS 12+ and ARM Linux VMs on Apple Silicon, including beta OS versions, without extra physical hardware.
- Running newer beta hosts (like macOS 26) requires VirtualBuddy 2.2 beta 2+ and device support files pulled from Xcode 27 beta or Apple's developer portal.
- APFS cloning (Command+D in Finder) lets you duplicate VMs almost for free in disk space, making it easy to snapshot and reset a clean environment.
- File sharing/clipboard sync work via the VirtualBuddyGuest app, with virtiofs mounting required manually for shared folders on macOS 13+ hosts.
This guide shows how to use Apple Silicon and the Gemma 3 270M model to fine-tune a small language model offline in under 10 minutes. It walks through installing the uv/MLX toolchain, preparing a simple code-review dataset, and running the training on any M-chip Mac without a cloud GPU.
- Gemma 3 270M runs on ~830MB RAM and hits 150+ tokens/sec on an M3, fine-tuning in under 10 minutes via MLX's uv toolchain (as fast as 3 minutes on an M1 Air)
- A dataset of only ~60 Python code snippets paired with expert review notes was enough to teach the model to flag security issues (SQL injection, unclosed files, insecure shell commands) and style problems
- The entire workflow runs offline on Apple Silicon with no cloud GPU or rental fees required
Google’s Gemini macOS app runs on Apple Silicon and sits in your menu bar, ready with a global shortcut (Option + Space) for instant AI assistance. It can share your active window for context-aware answers, sync chat history across devices, and offers tools for image and video creation.
- Gemini's new native macOS app only works on Apple Silicon running macOS Sequoia 15.0+, leaving Intel Macs unsupported
- Global shortcuts (Option+Space and Option+Shift+Space) let you summon Gemini for quick chat or full window access from anywhere
- With Accessibility permissions enabled, Gemini can read your active window's content to give context-aware help on code, spreadsheets, or documents
- It integrates Nano Banana for image generation and Veo for video creation, plus syncs chat history and memory across devices via your Google account