Apple released updated Mac mini and Mac Studio with new M6 and M5 Ultra chips, positioning them as machines for running large language models locally. The hardware refresh is purely specs-focused, but Apple's marketing now emphasizes AI workloads after macOS improvements last year made multi-Mac setups viable for distributed inference.

• Apple shipped macOS 26.2 in December with Thunderbolt 5 support for low-latency distributed AI inference, which triggered developer interest in daisy-chaining multiple Macs for larger models
• The M6 is Apple's first 2nm chip for Macs, and the M5 Ultra is now the most powerful option in the lineup, especially for AI tasks
• Developers and researchers are using stacked Mac minis and Studios as an alternative to expensive Nvidia GPU hardware for running local LLMs that exceed single-device capacity

The article breaks down which AI models and setups you can afford to run or train at home by 2026, comparing GPU costs, power use, and performance. It highlights efficient small-scale models, quantization tricks, and DIY hardware options to save money without sacrificing too much accuracy.

• Nvidia Blackwell cards should hit 150–200 TFLOPS FP16 under $1,500, making home rigs viable for large-model inference by 2026.
• 4-bit quantization plus FlashAttention already lets a 4090 run Llama 2-70B for under $0.02/inference and fit 13B models in 20GB VRAM, undercutting cloud A100 rental costs ($0.10–0.50/min).
• LoRA fine-tuning a 7B model on 8x4090s or two Blackwells takes a few hours and under $10 in electricity, though full 70B training from scratch still needs real clusters.
• A $3,000–4,000 home setup (with ~$50–100/month power costs for 24/7 use) will be enough to prototype LLM applications without cloud fees.