Apple Unveils M5 Ultra Mac Studio with 512GB RAM for Local LLMs
Apple introduced updated Mac Studio and Mac mini desktops featuring M5 Ultra and M6 chips. Offering up to 512GB of unified memory and 1.2TB/s bandwidth, the hardware targets developers running large open-weights models locally via MLX.
Impact: Medium
Why it matters
You can run 70B+ parameter open-weight models completely locally on a single desktop without cloud token fees or server cluster overhead.
TL;DR
- 01Mac Studio with M5 Ultra supports up to 512GB unified memory at 1.2TB/s bandwidth
- 02Thunderbolt 5 host networking enables multi-node MLX clusters for local inference
- 03High VRAM capacity allows running 70B+ parameter open-weights models locally without API token costs
Key facts
- M5 Ultra RAM Capacity
- Up to 512GB
- M5 Ultra Memory Bandwidth
- 1.2 TB/s
- M6 Mac Mini Starting Price
- $899
- M5 Ultra Mac Studio Starting Price
- $5,499
Hardware Specs for Local LLMs
Apple's M5 Ultra combines two M5 Max dies on a single SoC to provide unified memory capacity designed specifically for open-weights model inference:
- M5 Ultra Specs: 36 CPU cores, 80 GPU cores, up to 512GB unified memory
- Memory Bandwidth: Up to 1.2 TB/s
- M6 Chip Specs: 12 CPU cores (2 super cores, 4 performance, 6 efficiency), 12 GPU cores, up to 160 GB/s bandwidth, max 32GB RAM
- Storage & Connectivity: Up to 15 GB/s SSD speed, standard 2.5Gb Ethernet (upgradeable to 10Gb), Wi-Fi 7
Thunderbolt 5 Distributed MLX Inference
Using Thunderbolt 5 host-to-host networking in macOS, developers can link Mac mini and Mac Studio units to run multi-node distributed inference via the MLX array framework, providing an alternative to cloud GPU token pricing.
Pricing and Availability
- Mac mini (M6, 16GB): Starts at $899
- Mac Studio (M5 Ultra): Starts at $5,499 (512GB configuration ships late October)
Try it in 2 minutes
pip install mlx-lmbash
✓ When to use
- Building local AI dev workstations to cut recurring LLM cloud token spend
- Running privacy-sensitive codebases or massive 70B+ parameter models locally on MLX
✕ When NOT to use
- Standard light coding workflows where cloud API tiers or standard laptops are sufficient
- Workloads locked exclusively to NVIDIA CUDA features without Metal or MLX ports
What to do today
- Evaluate MLX multi-node setups if running open-weight models (DeepSeek, Qwen) locally
- Utilize Thunderbolt 5 links between Mac desktops for high-bandwidth distributed tensor parallelism
Sources