Run MLX inference, Mac AI deployments, and continuous integration on dedicated physical nodes with fixed specifications.
Dedicated Mac Mini M4 physical machine, not a virtual machine.Choose a location and billing term based on your team's location, code source, and task duration.
VMMini is built for engineering tasks that need Apple Silicon, fixed hardware specifications, and reproducible environments—not shared compute presented as a dedicated node.
Each order maps to one physical Apple Silicon node with fixed specifications. The CPU, unified memory, and local storage are not shared with other tenants, making it suitable for tasks that require a stable environment baseline.
Deploy MLX inference services, validate model execution on Apple Silicon, and record startup commands, health checks, response samples, and resource usage.
Run self-hosted runners, release builds, and experiment scripts by the day, week, month, or quarter. Before the task ends, export artifacts, revoke credentials, and clean the working directory.
MLX inference focuses on service ports, model processes, unified memory, and health checks. CI focuses on runner status, build queues, cache boundaries, and artifact export.
Current view: Check whether the model service has started, whether the port responds, and whether unified memory usage matches task expectations.
from mlx_lm import load, generate
model, tokenizer = load(MODEL_PATH)
GET /health 200
ready for inferenceThe billing term changes usage duration, not node hardware. Before placing an order, confirm the location, storage needs, and task exit plan together.
A dedicated Mac Mini M4 physical node for MLX inference, Mac AI deployment, iOS and macOS builds, and remote automation.
Run short-term validation by the day or week; schedule continuous builds, long-running experiments, and stable services by the month or quarter. All prices are in USD.
USDT-TRC20 and Visa / Mastercard / Amex via Stripe are supported. Available payment gateways are confirmed when the order is created.
Distance to the node is only a starting point. Before ordering, access the target location from your actual work network and consider your code repository, model source, artifact upload destination, and end users.
Suitable for teams in Southeast Asia and services accessed from the region. Test developer networks, code sources, and client endpoints separately.
Suitable for access paths in Japan and East Asia. For CI tasks, also verify dependency download and artifact upload directions.
Suitable for teams in South Korea and Northeast Asia. Test remote graphical access and SSH separately rather than relying on one conclusion.
Suitable for access paths toward southern China and Southeast Asia. Also evaluate where your models, repository, and build artifacts are located.
Break each workload into inputs, execution method, and expected artifacts to estimate the term, storage, and cleanup required before the task ends more accurately.
Ideal for validating Apple Silicon inference paths, deploying internal model services, and running reproducible response tests.
Suitable for release sprints, build peaks, and self-hosted runners that need an isolated cache boundary.
Suitable for batch processing, compatibility validation, data conversion, and engineering experiments that retain results by stage.
Do not copy code, data, and secrets together. Set validation criteria and rollback checkpoints at every step; proceed only after verification before making the next tool depend on the new node.
Pull code from a controlled repository and sync models and build inputs by directory. Configure secrets separately through controlled secret storage or environment variables; keep them out of repositories and ordinary archives.
Record macOS, Xcode, package manager, runtime, and dependency versions. Run a minimal build or inference request first, then import the full task and cache.
Register a self-hosted runner, restrict its repository scope, and set the artifact retention location. Validate with a non-critical branch first, then migrate release tasks.
Keep code and shareable configuration in the repository; use a controlled channel for secrets; manage models, caches, and build artifacts separately by lifecycle. Before ending the rental, verify that artifacts are exported, then clean the node data.
The node summary brings hardware, connection status, and the task queue into one run record. Fixed hardware does not eliminate workload management; you remain responsible for code, access credentials, service ports, and data cleanup after the task ends.
$ ssh node-m4-024
fingerprint verified
chip: Apple M4
memory: 16 GB
disk: healthyThe roadmap covers cost planning, service selection, remote debugging, build workflows, and container-tool boundaries. Completed articles will be added to the content hub by publication date.
Evaluate whether a short-term Cloud Mac fits your current workload, from temporary MLX inference experiments and release sprints to build peaks and short-term collaboration, with a framework for migration and exit planning.
Check whether the service uses a dedicated physical machine, whether specifications are fixed, whether the location is listed, how to test latency, how billing is structured, how to access logs, and how data is cleaned up after the task.
Define the boundaries for signing, logs, build artifacts, and physical-device connections in a remote environment, separating steps suited to a Cloud Mac from those that still require local test infrastructure.
Build a repeatable app-size optimization process around resource slicing, symbol files, duplicate dependencies, build caches, and release-artifact checks, while preserving auditable build records.
Cover dependency locking, signing configuration, self-hosted runner registration, and artifact archiving, with a focus on cache contamination, secret isolation, and retrying failed runs.
Compare differences in image builds, networking, disk usage, and automation scripts; learn to control caches without confusing container layers with physical host resources.
Detailed connection, build, MLX service, and troubleshooting procedures are organized by task in the Help Center.
Yes. VMMini M4 provides a dedicated Mac Mini M4 physical node with M4, 16GB RAM, and a 256GB SSD—not a virtual machine.
Choose a daily, weekly, monthly, or quarterly term based on the estimated task duration, allowing time for environment validation, artifact export, and data cleanup. The term does not change the hardware configuration.
Test the paths from your team to the node, from the node to the code source, and from service clients to the node. Singapore, Japan (Tokyo), South Korea (Seoul), and Hong Kong are all listed; actual availability is based on the real-time response when the order is created.
Prepare the order ID, node, time of occurrence, reproduction steps, and redacted logs. For an existing order, sign in to the console and submit a ticket so it can be linked to the node and event record.
VMMini M4 is available for daily, weekly, monthly, or quarterly rental. All orders are billed in USD. Before starting, confirm your data migration, credential management, and artifact export plans.