For full local inference with Apple’s MLX runtime, use two Mac Studio computers with M5 Ultra and 512GB of unified memory each. Meta also documents a four-node arrangement using four Mac Studios with 256GB each. The Macs must run macOS 26.2 or later, be connected directly with Thunderbolt 5 links between every pair, and each have enough local storage for the complete NVFP4 checkpoint. A hosted API is the simpler alternative if you do not want to buy and operate a multi-Mac cluster.
What Muse Spark Open requires
Muse Spark Open is an 816-billion-total-parameter, 42-billion-active-parameter model with a one-million-token context window, according to Meta AI Research’s October 15, 2026 announcement. Those figures describe a model far beyond the memory capacity of a typical personal computer. Although only 42 billion parameters are active for a given token, the full checkpoint and runtime state still have to be distributed across substantial unified memory.
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Meta’s Apple deployment is therefore a distributed system rather than a single-workstation installation. Apple’s unified memory is shared by the CPU and GPU, while MLX divides the model across multiple Macs and exchanges data over a high-speed interconnect. The documented recipe is not an optional performance tweak: it is the supported full-model layout for local MLX inference.
The two documented hardware layouts
| Layout | Hardware | Best fit | Main trade-offs |
|---|---|---|---|
| Two-node | 2 × Mac Studio with M5 Ultra and 512GB unified memory per computer | Primary documented local configuration | Highest memory capacity per unit and the cost of two top-memory systems; requires a direct Thunderbolt 5 connection between the pair |
| Four-node | 4 × Mac Studio with M5 Ultra and 256GB unified memory per computer | Splitting the workload across more nodes | Four computers, more direct links, more power and administration, and more opportunities for a configuration or cable problem |
| Hosted API | No local Mac cluster; access through a supported remote provider | People who need model access without provisioning hardware | Recurring provider charges, network dependence, remote data handling and less control over the serving environment |
Meta’s hardware documentation states: “Two Mac Studio computers with M5 Ultra and 512 GB of unified memory each, or four with 256 GB each.” Apple lists Mac Studio configurations with the M5 Ultra and up to 512GB of unified memory, so the two-node specification corresponds to an available hardware option rather than an unannounced configuration.
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How much unified memory do you need?
Two 512GB machines
The primary layout provides 1TB of installed unified memory across two nodes. That total is divided by MLX; it is not presented to macOS as one ordinary 1TB pool. Each Mac must still be able to hold its assigned model shards and runtime allocations, and the distributed software coordinates work between the nodes.
Four 256GB machines
The alternative also provides 1TB in aggregate, but in four smaller pieces. It can spread the same model across more computers when four 256GB Mac Studios are more practical to obtain than two 512GB systems. The trade is operational: four machines need four operating-system installations, four storage locations, more power and cooling, and a larger network of direct links.
Why 512GB in one Mac is not enough by itself
A single M5 Ultra Mac Studio with 512GB is not the documented full-model configuration for MLX. The official Apple deployment uses either two or four nodes. Installing the largest-memory single Mac does not turn it into a supported one-node Muse Spark Open server, and the documentation does not establish that one machine can load and serve the complete model locally.
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macOS version
Every Mac in the cluster needs macOS 26.2 or later. Before attempting model setup, open System Settings > General > Software Update on each computer and install a release that meets that minimum. Keep the nodes on the same current release where possible; a mixed software environment makes distributed troubleshooting harder even when the minimum version is technically met.
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Thunderbolt 5 topology
MLX distributes the model over RDMA on Thunderbolt 5. Meta requires a direct Thunderbolt 5 link between every pair of Macs. For two nodes, that means one direct cable connecting Mac A and Mac B. For four nodes, “between every pair” means six pairwise links: A–B, A–C, A–D, B–C, B–D and C–D.
This is a direct-computer topology, not a general-purpose Ethernet network. Do not assume that putting the Macs on the same Wi-Fi network, plugging them into a common router, or using an arbitrary USB-C cable satisfies the requirement. The cable, ports and negotiated connection must support Thunderbolt 5 end to end. Label each cable and document which pair it joins so a restart or move does not leave one link missing.
Physical planning
- Place the Macs close enough that certified Thunderbolt 5 cables can connect the required pairs without strain.
- Provide separate power connections and adequate ventilation for every Mac Studio; a four-node arrangement concentrates considerably more equipment in one location.
- Use identical account, security and update policies on all nodes so maintenance does not silently take one computer out of the cluster.
- Reserve time for reboots after macOS or framework updates, because all nodes must return to a compatible state before an inference job starts.
Storage and checkpoint preparation
Unified memory is only part of the requirement. Meta’s instructions also require checkpoint storage on each machine, with enough local disk space for the full NVFP4 checkpoint. Meta’s instructions do not state a numeric checkpoint size, so leave more free space than the downloaded file alone appears to need for temporary files, tokenizer data, caches and MLX runtime artifacts.
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Plan storage per node rather than assuming that one Mac’s internal disk can serve every machine. If the deployment process places a complete or partial checkpoint on each computer, each node needs its own capacity. Confirm the cookbook’s current placement rules before downloading; do not begin a multi-hundred-gigabyte transfer to a volume that cannot complete it.
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Keep the checkpoint on fast local storage for predictable startup and avoid disconnecting external volumes while a job is running. Back up the configuration metadata and download checksums separately from the model files so a failed transfer can be verified without repeating the entire setup.
Setting up the two-node configuration
- Choose the hardware. Obtain two Mac Studio systems with M5 Ultra and 512GB unified memory each. Confirm the memory option on the order or system information screen; a smaller-memory model is not the documented two-node specification.
- Update both computers. On each Mac, use System Settings > General > Software Update and bring macOS to version 26.2 or later. Restart both machines and verify the version again after the reboot.
- Prepare storage. Ensure each Mac has sufficient local free space for the NVFP4 checkpoint and associated runtime data. Use a filesystem and volume that remain mounted for the duration of inference.
- Connect the nodes directly. Attach one Thunderbolt 5 cable from the first Mac directly to the second. Do not route the link through a dock, hub or network switch unless Meta’s current cookbook explicitly says that device is supported.
- Install the current MLX and cookbook components. Follow Meta’s “Muse Spark Open on Mac” instructions for the versions and launch procedure current to your date of installation. Distributed inference depends on the framework recognizing both nodes, so do not substitute an unrelated single-Mac example.
- Stage the checkpoint on the required machines. Complete the cookbook’s download and placement steps on each node, then verify that the files are readable and complete before launching a long context.
- Validate the fabric before a large run. Confirm that the two Macs see the expected Thunderbolt connection and that MLX’s distributed startup reports both participants. Resolve a missing node or fallback transport before loading the full model.
- Run a small test first. Send a short prompt, check that both systems show activity, and confirm a generated response. Only then test the one-million-token context, which will place much larger demands on memory, storage and sustained interconnect throughput.
Setting up four Mac Studios
The four-node option follows the same software and storage preparation but adds topology work. Each of the four 256GB M5 Ultra systems must run macOS 26.2 or later and have the required checkpoint data. Build the six direct pairwise Thunderbolt 5 links before starting MLX, then identify each node consistently in the cookbook’s distributed-launch configuration.
Four nodes do not eliminate the need for high-memory machines; they divide the workload among 256GB systems. They also increase the number of components that can fail. A single disconnected cable, sleeping Mac, pending update or insufficient local disk can prevent the entire job from starting. Use a written inventory of node names, cable pairs, macOS versions, free space and MLX versions.
What the one-million-token context changes
The one-million-token context window is a capability limit, not a promise that every prompt will run at that length at the same speed. Longer prompts require additional key-value cache and sustained memory bandwidth. They also increase prompt-processing time and the amount of data exchanged while the model is split across Macs.
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Start with short contexts while validating the cluster. Increase context length gradually and watch free unified memory, disk pressure and thermals on every node. If a run fails only at larger lengths, reduce the context or concurrency before changing hardware; the failure may be cache capacity rather than an incorrect model installation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Local cluster or hosted API?
| Decision factor | Local two- or four-Mac deployment | Hosted API |
|---|---|---|
| Up-front investment | Requires multiple high-memory Mac Studios, Thunderbolt 5 cabling and storage capacity | No local cluster purchase |
| Ongoing cost | Electricity, maintenance, storage and hardware replacement | Provider usage or subscription charges recur with use |
| Control | Model files and inference remain on equipment you administer | Requests and data are sent to a remote service under its policies |
| Operations | You manage updates, cables, node health and failures | Provider manages serving hardware; you depend on its availability and limits |
| Connectivity | Inference can continue without an internet connection after setup, subject to local services | Requires a reliable internet path to the provider |
Choose local hardware when privacy, offline operation, predictable control or repeated heavy usage justifies the capital and administration. Choose a hosted API when the priority is access without purchasing, wiring and maintaining four or two high-memory computers. A meaningful cost comparison needs your expected token volume, provider rate, electricity price, hardware depreciation and the value of your time; no universal break-even point follows from the hardware specifications alone.
Common failure modes and fixes
The launcher sees only one Mac
Check the direct Thunderbolt 5 cable, port seating and macOS version on both nodes. A missing participant usually indicates a transport, discovery or software-version problem rather than insufficient model memory. Reboot both Macs after correcting the link and repeat the small distributed test.
The model downloads successfully but will not start
Verify free local disk on every node and confirm that the NVFP4 files are present where the cookbook expects them. A complete download on one Mac does not prove that the other node has its required shard or files.
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A four-node run fails while a two-node run works
Audit all six pairwise links, not just the first cable you installed. Check that no Mac is asleep, updating or disconnected, and that each node reports the same macOS and MLX environment. Four-node deployment has more failure points than the two-node layout.
Short prompts work but long prompts fail
Reduce context length or simultaneous requests and inspect memory pressure on all Macs. The one-million-token maximum consumes substantially more cache than a short test, so successful startup does not guarantee that the longest context will fit under every workload.
Buying checklist
- For the primary layout: two Mac Studios, each with M5 Ultra and 512GB unified memory.
- For the alternative: four Mac Studios, each with M5 Ultra and 256GB unified memory.
- macOS 26.2 or later installed on every node.
- Direct Thunderbolt 5 links between every pair of Macs.
- Enough local disk space on each machine for the NVFP4 checkpoint and runtime files.
- A plan for ventilation, power, cable labeling, updates, backups and node monitoring.
- A decision on whether local control outweighs the capital and operational cost of a cluster.
Bottom line for each reader
If you want the documented local setup
Buy the two-node M5 Ultra configuration with 512GB unified memory per Mac, then satisfy the macOS, Thunderbolt 5 and per-node storage requirements before installing MLX.
If 512GB systems are impractical
Use four M5 Ultra Mac Studios with 256GB unified memory each, accepting the six-link topology and higher operational overhead.
If you do not want to run a cluster
Use a hosted API and treat provider pricing, privacy terms, internet dependence and usage limits as the trade-offs for avoiding local hardware.
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