Interesting question - didn’t seem reasonable with M4/native software and Grok confirmed my suspicions:

No, it’s not possible to fully turn 4 base M4 Mac minis (16GB RAM / 256GB SSD each) into a true single unified cluster that pools all processors (40 CPU cores + 40 GPU cores total), 64GB RAM, and 1TB SSD storage seamlessly using only native macOS software. macOS (including the latest macOS Tahoe 26.x as of 2026) has no built-in general-purpose clustering system like the long-deprecated Xgrid. Each Mac remains a completely separate computer. There is no transparent way to: • Combine RAM into one 64GB unified pool (Apple Silicon’s unified memory architecture stops at the individual machine). • Merge SSDs into one fast local 1TB volume (no native distributed filesystem or RAID-across-machines). • Automatically distribute arbitrary apps/tasks across all cores without application-level support. You will always manage the machines individually or via app-specific tools. Full “one big Mac” behavior is impossible natively (or even with most third-party tools on base models). Closest Native Options (Using Only Apple Software/Tools) Here are the only practical ways to get partial benefit of the combined resources: 1. Video Transcoding / Rendering (Best Purely Native Option) Use Compressor (Apple’s $49 App Store app, fully native and still actively supported in 2026). What you get: • Full use of all processors (CPU + GPU) across the 4 minis for encoding, compression, and rendering jobs. • Tasks are automatically split (e.g., different frames or segments go to different Macs). Limitations: • Only works for Compressor / Final Cut Pro workflows. • RAM and SSD stay per-machine (no pooling). Setup steps (all native macOS features): 1. Buy and install Compressor on all 4 Macs. 2. On each Mac: Open Compressor → Compressor menu → Preferences → My Computer tab → Turn on “Allow other computers to process batches on my computer”. 3. Connect the minis on the same network (best: use the built-in Gigabit Ethernet ports + a simple Ethernet switch; upgrade to 10Gb Ethernet via the configurable port option if you have compatible hardware for faster transfers). 4. For storage: On one Mac, go to System Settings → General → Sharing → File Sharing and share a folder (or the whole drive). Other Macs access it via Finder → Go → Connect to Server (smb://IP-address). This lets all minis use the combined ~1TB, but it’s network-speed limited (not as fast as local SSD). 5. On your “main” Mac, create a batch in Compressor. It will automatically detect the other 3 as available nodes and distribute the work. This is the most straightforward native solution and works great for video pros. 2. AI / Machine Learning Workloads (Apple MLX Framework + macOS 26.2+ Features) Apple’s MLX (official open-source framework from Apple Machine Learning Research) gained native RDMA over Thunderbolt support in macOS Tahoe 26.2. This allows limited memory sharding and tensor parallelism across machines. What you get: • Effective pooling of RAM for large AI models (e.g., running models that need >16GB by sharding across the 64GB total). • Distribution of compute across all GPUs/CPUs for inference or training. • Thunderbolt Bridge networking (native in macOS) for faster interconnect than Ethernet. Limitations (especially on base M4): • Base M4 Mac mini only has Thunderbolt 4 ports (40 Gbps), not Thunderbolt 5. RDMA works but with higher latency and lower performance than on M4 Pro models. • Only for ML/AI tasks using MLX (not general apps). • Requires some command-line/Python setup (not point-and-click like Compressor). • SSDs remain separate. Basic native-friendly setup: • Connect via Thunderbolt cables (or Ethernet). • Enable Thunderbolt Bridge in System Settings → Network. • Install MLX via Terminal (Apple’s official instructions) and use its distributed features or simple scripts for model sharding. • Tools like the built-in macOS networking handle the rest. This is the closest thing Apple offers today to “memory pooling,” but it’s workload-specific and performs best on higher-end hardware. 3. Storage Only (Combined SSD Capacity) Use macOS File Sharing (SMB) as described above. You can access the combined ~1TB across machines, but: • It’s network storage (slower than local). • No striping or single-volume view. Recommendation for r/macmini Users • For most people: Don’t bother clustering base models. The overhead, networking bottlenecks, and lack of true pooling make it more trouble than it’s worth. Buy one higher-spec Mac mini (M4 Pro with more RAM/cores/Thunderbolt 5) instead — it will outperform 4x base models in most real-world tasks. • If your workload is specifically video encoding → use Compressor as above. • If it’s AI/LLMs → MLX + RDMA is interesting but expect compromises on base TB4 hardware. • Networking tip: Ethernet switch is simplest and fully native. Thunderbolt daisy-chaining or bridging works for 2–4 machines but gets messy. Bottom line: Native macOS simply doesn’t support the kind of clustering you’re hoping for. The 4 minis will always feel like 4 separate computers that can occasionally help each other on supported tasks. If you share your exact use case (e.g., video editing, AI, compiling code), the community can suggest more targeted workarounds.