Every order maps to an independent physical Mac mini node. Keep Xcode, dependencies, caches, and automation scripts consistent so daily development, build queues, testing, and MLX inference run in a reproducible environment.
Development inputGit, dependencies, and build parameters
Node executionXcode, scripts, and caches
Artifact outputArchives, logs, and test reports
Cloud Macs are not a one-size-fits-all solution. Project size, concurrent tasks, unified-memory peaks, dependency caches, and graphics interaction all affect the right configuration.
Ideal for individual developers who need fixed Xcode versions, command-line tools, package managers, and project caches. Pull code through Git and keep builds, tests, and archives on the same node.
Ideal for pinning build executors to a dedicated physical machine, avoiding contention for compute, memory, and local storage. Teams manage the toolchain, cache policy, and execution labels centrally.
Ideal for validating multiple Xcode versions, running command-line tests, auditing app packages, and reviewing release candidates. Record the OS version, tool versions, commit, and command parameters for every test to make differences reproducible.
Ideal for model loading, quantization validation, inference, and data preprocessing with MLX. Estimate the combined use of model weights, runtime caches, and input data in unified memory before choosing a configuration.
Stable builds depend on more than chip speed. The commit, Xcode version, dependency lockfile, signing materials, environment variables, and export parameters must all be recorded together, or the same project may produce different results.
Use a branch, tag, or commit hash to identify the input version. Record repository status and the commit at the start of the build log to prevent uncommitted changes from entering the archive.
Check the dependency lockfile first, then restore package-manager caches. Group caches by Xcode version, architecture, and dependency digest, with a fallback to a full install when a cache misses.
Treat certificates, provisioning profiles, and keys as controlled inputs rather than storing them in the repository. Before execution, output only names, validity status, and digests; never expose sensitive content in logs.
Specify the workspace, scheme, configuration, and destination. On failure, retain the exit code, complete build log, and test-results directory instead of capturing only the last line.
After archiving, export the target artifacts and generate a record of file sizes, verification digests, commit, Xcode version, and build parameters for delivery and later comparison.
Dedicated physical nodes provide clear resource boundaries: compute, memory, and local storage are not shared with other customers. Teams still need to design queueing, concurrency limits, cache invalidation, and retry rules.
Track peak simultaneous jobs, per-job memory peaks, cache size, and average artifact size. When many short jobs are queued, scheduling matters more than single-machine peak capacity; large workspaces should prioritize memory headroom.
Whatever self-hosted executor you use, script the installation steps, service-account permissions, working directory, and cleanup policy. Do not depend on a node state created by one-off manual changes.
Manage dependency caches, DerivedData, and intermediate artifacts separately. Generate keys from tool versions and lockfile digests so stale caches cannot produce apparently successful builds with inconsistent binaries.
These are starting points, not fixed build-time guarantees. Module count, dependency types, test targets, and cache-hit rates all affect actual results.
Ideal for lightweight single-job builds, code checks, and small-project validation.
Ideal for daily development, multi-module projects, and more reliable multitasking.
Ideal for high-concurrency builds, large workspaces, and memory-intensive jobs.
SSH is suited to code checkout, dependency installation, script execution, log reading, and file sync; remote desktop is suited to Xcode project settings, UI debugging, and graphical tools. Use consistent project directories and permission boundaries for both entry points.
Connect with keys, verify the host fingerprint, and restrict private-key permissions. Put long-running jobs in a resumable session so a brief local network interruption does not hide their status.
Organize work with commits, branches, and tags rather than placing build artifacts directly in the source directory. Plan separate transfer and versioning strategies for large binary assets.
Export a restorable package list and separately record tools requiring manual configuration. After migration, verify paths, architecture, and command versions before resuming automation.
Choose the workflow based on repository size and network conditions. Keep frequently read and written small files on the node, and export artifacts and logs centrally when each job finishes.
Model weights are only part of the footprint. Runtime caches, intermediate tensors, input context, data preprocessing, and other concurrent processes also use unified memory. Measure the actual peak and leave room for the system and tools.
Estimate the base footprint from parameter count and quantization method, then confirm the actual downloaded file size.
Context length, batch size, and inference-framework implementation can change the peak; file size alone is not enough.
Preprocessing, decoding, and result storage may use memory and disk simultaneously, so testing should cover the full input pipeline.
Reserve space for macOS, the Python environment, monitoring commands, and remote sessions to avoid frequent swapping near the limit.
Pin the Python and MLX versions, use a small input to verify model loading, inference output, and memory-monitoring commands, and confirm the environment works end to end.
Increase context length, batch size, and parallel jobs step by step while recording peak unified memory, disk changes, and failure conditions.
Different quantization methods change memory use, output quality, and execution performance. Keep inputs and parameters identical for a meaningful comparison.
Save dependency lists, model digests, inference parameters, and data versions. When sharing results, include test conditions rather than only a single number.
After a Unity project is exported to iOS, issues may come from asset import, plugin processing, Xcode project generation, dependency installation, or signing and archiving. Saving logs by stage locates failures faster than repeatedly clicking Build.
Import assets in a fixed editor version and record platform switches, texture processing, and script-compilation results. Estimate the disk footprint of large Library caches separately.
Pin export parameters and the target directory, then verify that plugin scripts and native dependencies are written to the project as expected. Record the source commit and asset version for every export.
Keep signing materials separate from project source and provide them through controlled directories or automation variables. Log only identifiable configuration names; never output sensitive content.
Specify the workspace, scheme, configuration, and export options. Retain the complete Xcode log, exit code, and archive directory structure.
Generate a verification digest and record file sizes. After transfer, verify file integrity, then clean up intermediate directories and old caches according to team policy.
Do not mix Unity Library, dependency caches, DerivedData, archives, and exported artifacts in one uncontrolled directory. Define retention conditions and cleanup methods for each cache type.
The base project size is only the starting point. Asset imports, Xcode exports, intermediate files, debug symbols, and multiple archive versions can consume space simultaneously.
Passing tests does not mean release inputs are fixed. The commit, Xcode version, dependency digest, signing configuration, test targets, and export options must remain consistent throughout release-candidate preparation.
| Check stage | Confirm | Recommended records | When differences appear |
|---|---|---|---|
| Multi-version Xcode validation | Compiler, SDK, and command-line tool paths | Version output and build parameters | Rebuild in a separate directory without reusing suspect caches |
| Command-line testing | scheme, destination, test scope | Exit code, result bundle, and failure log | Isolate the failing case first, then separate environment issues from code issues |
| Package inspection | Assets, architectures, dynamic libraries, and debug symbols | File list, size, and digest | Compare structural changes with the previous candidate |
| Signing and export | Configuration name, target, and export options | Redacted configuration record and archive log | Stop retrying and verify that the inputs match |
| Environment freeze | Commit, dependency lockfile, and tool versions | Environment inventory and recovery steps | Route every change back through validation |
Use a specific commit or tag, confirm the workspace has no uncommitted changes, and save submodule and dependency-reference states.
Record macOS, Xcode, command-line tool, package-manager, and key script versions to prevent automatic upgrades during release preparation.
Save archives, export logs, test results, file sizes, and verification digests so later reviews have consistent evidence.
Choose a starting point from the typical workload, then monitor memory peaks, cache size, and queue wait times on the real project. Upgrade based on a specific bottleneck, not just the job name.
Lightweight build starter
Ideal for small projects, single-job Xcode builds, command-line tests, code checks, and short validation cycles. Check remaining storage early if caches or local data keep growing.
Choose OnceMac M4 16Daily development and multitasking
Ideal for medium-sized multi-module projects, daily remote development, CI executors, and workloads needing more cache space. Set clear limits for parallel processes.
Choose OnceMac M4 24High-concurrency and memory-intensive workloads
Ideal for large workspaces, high-concurrency builds, large Unity asset projects, and MLX large-model inference. Confirm the model, quantization method, and actual memory peak before choosing.
Choose OnceMac M4 Pro 64Confirm that the chip and memory cover the peak, base storage accommodates the project and caches, the node is close to primary users, the rental period covers the full job cycle, and logs and artifacts have an export path.
OnceMac offers 3 available configurations and 6 node options, with all orders settled in USD. Actual availability is shown in real time in the console.