PRODUCT VIEW / ANONYMIZED

From Physical Node to Build Artifact: This Is the Real Workspace

This is not a decorative data-center photo gallery. It breaks down MacWorker in the order you actually use it: choose a dedicated physical Mac, confirm the region and term, get your connection details, then develop, build, or run compatible AI inference workloads in a persistent macOS environment.

Rent a Dedicated Physical Mac Now View Models & Fixed Pricing

Apple Silicon physical node
Single-tenant dedicated resources
GUI and command-line access

MW NODE VIEW ONLINE

NODE-7C••

NODE-3A••

NODE-9F••

FORGE M4

Resources: M4 · 16GB · 256GB
Region: Japan (Tokyo)
Connection: Desktop · SSH
Status: READY

Anonymized architecture view: node IDs, network addresses, and login details are hidden.

HARDWARE GALLERY

The Mac assigned to you—not a slice of shared capacity

Every order maps to a dedicated physical machine. Its processor, memory, and local storage are not shared with other tenants. The portal shows the device through its node ID and connection status instead of placing multiple users inside one virtual instance.

MW / PHYSICAL NODE

Dedicated physical machine

Device-level resource boundaries

During the term, your selected model is delivered as a complete physical node. Forge M4 includes M4, 16GB memory, and 256GB SSD; Studio M4 includes M4, 24GB memory, and 512GB SSD; Atlas M4 Pro includes M4 Pro, 64GB memory, and 2TB SSD.

CPU and memory are not contended with other tenants
System environment, dependency caches, and project directories remain available
Export build artifacts and project data before releasing the node

NODE LABEL READY

JP · NODE-7C••

Model: Forge M4
Term: Monthly
Access: 2 methods

Labels expose only the information needed to operate the node

The console shows the model, region, term, node status, and connection entry points. Account details, full addresses, and authentication data do not appear in the public view.

SG-••ONLINE

JP-••ONLINE

US-E-••ONLINE

Region is a scheduling parameter, not a promise of a shared pool

Singapore, Japan (Tokyo), South Korea (Seoul), Hong Kong, and the U.S. East are offered according to real-time inventory. Choose a region based first on your team’s connection latency, code source, and artifact destination.

All nodes run normally 365 days a year with continuous availability. Immediate delivery depends on the real-time status of the selected model and region; the portal confirmation is authoritative.

PORTAL CONFIGURATION

The portal breaks your purchase decision into four fields you can verify

Choose the fixed hardware first, then select the region, term, and add-ons. Review each item before submitting instead of guessing memory, storage, or delivery location from an unclear package name.

MW Node Configuration

ORDER DRAFT

STEP 1 / 4

Choose a Physical Node

Configuration Example

Forge M4SELECTED

M4 · 16GB · 256GB SSD

Studio M4

M4 · 24GB · 512GB SSD

Atlas M4 Pro

M4 Pro · 64GB · 2TB SSD

Region Japan (Tokyo) Other options: Singapore, Seoul, Hong Kong, U.S. East

Term Monthly Daily, weekly, or quarterly options are also available

Add-on +1TB SSD Matches the selected service term

Payment U.S. dollars (USD) Two specified payment methods supported

Configuration is not inferred from abbreviations

Each model card lists the chip, memory, and SSD capacity. Extra storage and Thunderbolt 5 parallel access enter the order only when explicitly selected.

Access is managed in one place after delivery

Once the node is ready, its status, login details, browser-based remote desktop, and SSH information are available in the same order context, so you do not have to assemble parameters across pages.

Public views are anonymized by default

This page hides order IDs, full node IDs, network addresses, usernames, and authentication data. The live console provides corresponding data only to authorized signed-in users.

DEVELOPER DESKTOP

View your project, commands, and resource changes in one desktop

The development desktop uses a familiar four-zone layout: project navigation, editor, build logs, and resource monitoring. You arrange the actual windows yourself, with the full macOS GUI and command line available.

BuildPipeline.swift Package.swift

struct BuildPipeline {
let scheme = "SampleApp"
let configuration = "Release"
func archive() async throws {
try await prepareDependencies()
try await runTests()
try await exportArtifact()
}
}

BUILD LOGEXIT 0

$ xcodebuild -scheme SampleApp -configuration Release archive
Resolve Package Graph
CompileSwiftSources normal arm64
Run unit tests: 128 passed
Archive path: ./Artifacts/SampleApp.xcarchive
BUILD SUCCEEDED

Your development environment stays in place

Dependency directories, tool versions, build caches, and scripts can remain on the same physical node. With weekly, monthly, or quarterly use, you do not need to rebuild the entire environment at the start of every session.

GUI work and automation run side by side

Use the browser-based remote desktop for interactive checks; run batch builds, log collection, and file operations over SSH. Both entry points target the same node, not two isolated environments.

Choose resources based on task history

Track build duration, peak memory, remaining disk space, and failure logs together. If memory stays near its limit, evaluate Studio M4 or Atlas M4 Pro from Forge M4 instead of choosing by task name alone.

CI/CD EXECUTION PATH

A build moves through four clear stages from commit to artifact

The dedicated node performs the actual work. Your team still configures code hosting, triggers, and artifact storage through its existing toolchain; MacWorker provides a remotely controllable physical macOS execution environment.

01

Code Commit

The developer pushes the designated branch or tag. Each commit should include reproducible dependency lists, build settings, and version information so the node does not depend on undocumented manual context.

Input
commit / tag

Check
Branch and configuration
02

Job Trigger

The pipeline creates a job from a branch, schedule, or manual action. Before scheduling, it checks node availability, the working directory, and concurrency policy, then sends commands to the designated execution node.

Input
job payload

Check
Queue and status
03

Physical Node Execution

The dedicated Mac pulls code, restores caches, resolves dependencies, compiles, and runs tests. Your team can pin tool versions and use logs to identify the exact command, exit code, and duration.

Input
source + cache

Output
log + result
04

Artifact Output

Archive files, test reports, and logs are written to the agreed directory, then uploaded by your team’s scripts to its own artifact system. Before release, verify the file digest, build number, and test results.

Input
build result

Check
hash + report

RUN #1842 At least six types of information remain for a traceable run

Commit identifier
Node identifier
Start and end times
Tool versions
Exit code
Artifact digest

AI INFERENCE WORKSPACE

AI experiments are about reproducibility—not piling on undefined capabilities

Suitable for model inference and validation tasks that run on the selected Apple Silicon configuration. This page does not promise specific model speeds, training capabilities, or untested framework compatibility; choose based on model format, peak memory, and real samples.

FILES experiment-014

▾models
└model-q4.bin
▾configs
└inference.yaml
▾scripts
└run_inference.py
▾results
└run-014.json

TERMINAL arm64 · zsh

$ python run_inference.py \
  --config configs/inference.yaml \
  --model models/model-q4.bin \
  --output results/run-014.json

runtime: arm64
samples: 120
warmup: 5
result: archived
status: completed

OBSERVATION RUN 014

Peak memory41.8GB

Sample count120

Run statusCOMPLETED

ARCHIVE 4 ITEMS

Configuration: inference.yaml
Dependencies: environment.lock
Result: run-014.json
Digest: sha256••••8c1a

Pin model and dependency versions first

Place the model file, configuration, run script, and dependency lockfile in clearly defined directories. The experiment result should answer which version and parameters were used, and on which node it ran.

Record peak values, not averages

Choose memory based on the peak during model loading and inference. The values shown here explain the observation structure only; they are not fixed performance figures for any model on every node.

Then archive the inputs and result digest

At minimum, the result directory should retain the configuration, dependency versions, run log, output file, and digest. For cross-node comparisons, keep the samples, parameters, and measurement method consistent.

Scope: MacWorker provides three fixed configurations—Forge M4, Studio M4, and Atlas M4 Pro. Whether a model runs depends on its format, framework compatibility, memory requirements, and your own environment configuration; this page does not imply extra acceleration hardware or unlimited resources.

VISUAL DISCLOSURE

Every public interface applies data minimization first

Content Hidden

Account name, email, and organization details
Full order and node identifiers
Network addresses, usernames, keys, and passwords
Project names, source code, and real build artifacts
Payment credentials and transaction identifiers

Structural Information Retained

Configuration order for model, region, term, and add-ons
Location of node status and connection entry points
Project, log, and monitoring zones in the development desktop
Execution path from commit to artifact
Relationships among AI experiment files, commands, observations, and archives

When reading these screens

Examples explain the information architecture and workflow; they do not constitute a fixed UI promise. Portal fields, button placement, and visual details may change as the product evolves, but the model, region, term, and add-ons in a confirmed order remain governed by the portal record.

Monitoring curves, run numbers, node identifiers, and experiment results are anonymized demonstration data and should not be used as performance benchmarks.

READY TO CONFIGURE

Choose a dedicated physical Mac for the task at hand

If you know your peak memory, workspace size, task duration, and team location, configure the model, region, term, and add-ons directly. If not, review the use-case recommendations and fixed pricing matrix first.

Rent a Dedicated Physical Mac Now View Resource Recommendations by Task