CI/CD Pipeline Infrastructure on Dedicated Servers

Shared CI services throttle at the worst possible moment. GitHub Actions free tier queues jobs when concurrent usage spikes. GitLab shared runners time out on builds that take longer than an hour. Paid tiers add up fast: GitHub Actions charges $0.008 per minute for Linux runners, which means a 20-minute build running 50 times per day costs $240 per month before larger parallel test suites enter the picture.

A dedicated server running Jenkins or self-hosted GitLab runners changes the economics entirely. Fixed monthly cost. No per-minute billing. No queue wait during peak hours. And on NVMe-backed storage, Docker layer caching and test artifact reads run at speeds that make a measurable difference in total pipeline duration.

Why Dedicated CI Infrastructure Makes Sense at Scale

The Queue Problem

Shared CI services operate on fair-use queuing. When your team pushes 15 commits simultaneously before a release, those jobs queue behind each other. On a 16-core dedicated server running Jenkins with 16 parallel executors, all 15 jobs start simultaneously. The wall-clock time from last commit to final build result drops dramatically.

That compression matters most during code review cycles, where developer wait time directly affects how many review iterations happen per day. Teams that cut CI wait time from 15 minutes to 3 minutes typically see higher PR throughput and faster merge cycles.

Per-Minute Billing vs. Fixed Cost

A mid-size engineering team running 200 builds per day at an average of 15 minutes each accumulates 3,000 build-minutes daily. At GitHub Actions pricing of $0.008 per minute for Linux, that is $24 per day, roughly $720 per month. On GitLab’s Premium tier with additional runner minutes purchased, similar costs apply.

An InMotion Hosting Essential Dedicated Server at $X per month runs the same 3,000 daily build-minutes with capacity to spare. At the Advanced tier, test parallelization across 64GB of RAM handles comprehensive test suites without memory pressure.

Jenkins: Master/Agent Topology on Dedicated Hardware

Single-Server Setup

For teams running fewer than 500 builds per day, a single dedicated server running both the Jenkins master and build agents is practical. The Jenkins master process is lightweight: it handles scheduling, plugin management, and the web UI. Build agents do the actual work.

Configure Jenkins with 12-14 build executors on a 16-core server, leaving 2 cores for the master process and OS overhead. Each executor runs one build job. With 14 parallel executors, a queue of 14 jobs clears in the time a single job takes.

Multi-Server for Larger Teams

When build volume grows above 500 daily jobs, or when different build environments need isolation (Python 3.9 vs. 3.12, different Docker daemon versions), a master-agent topology across multiple dedicated servers provides cleaner separation. The Jenkins master runs on a smaller server (Essential tier is sufficient). Build agents run on higher-spec servers matched to workload requirements.

Agent servers connect to the master via SSH or JNLP. This allows adding capacity incrementally: a second Advanced dedicated server doubles build throughput without reconfiguring the master.

GitLab Runners on Dedicated Hardware

GitLab self-hosted runners register against a GitLab instance (either gitlab.com or self-hosted) and execute pipeline jobs. Each runner process can handle one job at a time; running multiple runner processes on a 16-core server achieves parallelism.

The GitLab runner configuration for maximum parallelism on an Extreme server:

concurrent = 16 (in /etc/gitlab-runner/config.toml; sets maximum parallel jobs across all registered runners)

executor = docker (Docker executor isolates each job in a fresh container, preventing state bleed between jobs)

pull_policy = if-not-present (uses locally cached Docker images rather than pulling on every job; critical for NVMe cache performance benefit)

With the Docker executor and local image caching on NVMe, subsequent runs of the same pipeline skip the image pull entirely. A Python 3.12 image that takes 45 seconds to pull from Docker Hub runs from local NVMe cache in under 2 seconds.

NVMe Storage: Where CI Performance Improves Most

Docker Layer Caching

Docker builds are layered. When a build changes only application code but not dependencies, Docker reuses cached layers for the dependency installation steps. This cache lives on the runner’s local storage. On SATA SSD, reading a 2GB cached layer takes roughly 4 seconds. On NVMe at 5GB/s, the same read takes under half a second.

For a build that runs 20 pipeline jobs per day each using cached Docker layers, the difference accumulates to several minutes of saved wall-clock time daily. Across a team of 20 developers, that is meaningful.

Test Artifact Storage

Test suites generate substantial artifact output: coverage reports, screenshots from browser tests, compiled binaries, test result XML files. On a busy CI server, hundreds of artifact writes per hour hit the storage layer. NVMe handles this write load without I/O wait accumulating in the build logs.

Configure Jenkins or GitLab to store artifacts on the local NVMe volume during the build, then upload final artifacts to object storage or a shared repository at pipeline completion. This two-stage approach keeps the build fast while preserving artifacts beyond the server’s local capacity.

Test Parallelization and Scratch Space

Modern test frameworks distribute tests across multiple processes. pytest-xdist, Jest’s –maxWorkers flag, and RSpec’s parallel_tests gem all write temporary files to local storage during parallel test execution. On NVMe, 16 parallel test workers writing temp files simultaneously do not create I/O contention. On SATA SSD or network storage, they frequently do.

Configure test temp directories to explicitly point at the NVMe mount: TMPDIR=/mnt/nvme/tmp for shell-based test runners, or framework-specific temp directory configuration. This is a one-line change that eliminates a common source of flaky parallel test failures.

Build Caching Strategies

Dependency Caches

The most expensive step in most CI pipelines is dependency installation: npm install, pip install, Maven dependency resolution. These steps pull packages from the internet and write them to local cache directories.

npm: Cache node_modules and the npm cache directory between builds using Jenkins’ stash or GitLab’s cache key

pip: Cache the pip download cache (~/.cache/pip) and use –find-links to serve from local NVMe cache

Maven: Cache ~/.m2/repository between builds to avoid re-downloading JAR dependencies

Gradle: Cache ~/.gradle between builds; Gradle’s build cache additionally caches task outputs

On a dedicated server with NVMe storage, these caches persist between jobs naturally. The challenge on shared CI services is that caches must be uploaded and downloaded between every job, adding overhead. On your own server, the cache is always local.

Automated Testing Environments

Docker Compose for Integration Tests

Integration tests frequently need external services: databases, message queues, mock APIs. Docker Compose spins up these service dependencies per test run. On a dedicated server with 192GB RAM and 16 cores, running PostgreSQL, Redis, and a mock API server in Docker alongside the actual test suite adds minimal overhead.

Configure Docker Compose to use named volumes backed by the NVMe volume for service data. PostgreSQL with a named volume on NVMe initializes a test database in under 1 second vs. 5-8 seconds on slower storage.

Browser Testing

Playwright and Cypress browser tests are resource-intensive: each browser context uses 200-400MB of RAM and meaningful CPU time for rendering. On a shared CI runner, browser tests frequently time out or produce flaky results under memory pressure. On a dedicated server with 192GB RAM running 8 parallel browser test workers, each worker has ample memory and there is no external pressure on resource allocation.

Comparison: Shared CI vs. Dedicated Server