--- title: "GitHub-Hosted Runners Reference (2025)" description: "This reference covers all GitHub-hosted runner types, including recent additions and deprecations." type: skill canonical_url: https://claudary.paisolsolutions.com/skills/runners source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:46:17.800Z license: CC-BY-4.0 attribution: "GitHub-Hosted Runners Reference (2025) — Claudary (https://claudary.paisolsolutions.com/skills/runners)" --- # GitHub-Hosted Runners Reference (2025) This reference covers all GitHub-hosted runner types, including recent additions and deprecations. ## Overview # GitHub-Hosted Runners Reference (2025) This reference covers all GitHub-hosted runner types, including recent additions and deprecations. ## Standard Runner Labels ### Ubuntu ```yaml runs-on: ubuntu-latest # Ubuntu 24.04 (default) runs-on: ubuntu-24.04 # Ubuntu 24.04 runs-on: ubuntu-22.04 # Ubuntu 22.04 runs-on: ubuntu-20.04 # Ubuntu 20.04 ``` ### Windows ```yaml runs-on: windows-latest # Windows Server 2022 (default) runs-on: windows-2025 # Windows Server 2025 (NEW) runs-on: windows-2022 # Windows Server 2022 runs-on: windows-2019 # Windows Server 2019 ``` ### macOS ```yaml runs-on: macos-latest # macOS 15 (as of Aug 2025) runs-on: macos-15 # macOS 15 Sequoia (Apple Silicon) runs-on: macos-14 # macOS 14 Sonoma (Apple Silicon) runs-on: macos-26 # macOS 26 (PREVIEW) ``` --- ## macOS Runner Updates and Deprecations ### Current Status | Label | Status | Architecture | Notes | |-------|--------|--------------|-------| | `macos-latest` | **Active** | ARM64 (Apple Silicon) | Points to macOS 15 | | `macos-15` | **Active** | ARM64 (Apple Silicon) | M1/M2/M3 | | `macos-14` | **Active** | ARM64 (Apple Silicon) | M1/M2 | | `macos-26` | **Preview** | ARM64 (Apple Silicon) | Beta | | `macos-13` | **RETIRED** | Intel x86_64 | Retired November 14, 2025 | | `macos-12` | **RETIRED** | Intel x86_64 | Retired | ### Intel-Specific Labels (Long-term Deprecated) ```yaml runs-on: macos-15-intel # Intel x86_64 (NEW but deprecated long-term) runs-on: macos-14-large # Intel x86_64 runs-on: macos-15-large # Intel x86_64 ``` **Important:** Apple Silicon (ARM64) will be required after Fall 2027. Plan migration now. ### Migration Example ```yaml jobs: build: # BAD - macos-13 retired Nov 14, 2025 (WILL FAIL) # runs-on: macos-13 # GOOD - Use macos-15 or macos-latest runs-on: macos-15 steps: - uses: actions/checkout@v6 - run: ./build.sh ``` --- ## ARM64 Runners ### Availability - **Generally available** as of August 2025 - **Free** for public repositories - **Private repositories** require GitHub Enterprise Cloud plan ### Labels ```yaml runs-on: ubuntu-latest-arm64 # Free for public repos runs-on: ubuntu-24.04-arm64 runs-on: windows-latest-arm64 # ARM Windows ``` ### Example ```yaml jobs: build: runs-on: ubuntu-latest-arm64 # Free for public repos steps: - uses: actions/checkout@v6 - name: Build on ARM64 run: | uname -m # Should output: aarch64 ./build.sh ``` ### Specifications - 4 vCPU ARM64 processors - Available for Linux and Windows - Native ARM execution (no virtualization needed) - Ideal for multi-architecture builds ### Common Validation Issues - Using ARM64 runners in private repos without Enterprise Cloud - Assuming all community actions work on ARM64 - Not testing ARM-specific compilation issues --- ## GPU Runners ### Availability - **Generally available** for Windows and Linux - Requires **Team** or **Enterprise Cloud** plan ### Labels ```yaml runs-on: gpu-t4-4-core # NVIDIA Tesla T4 ``` ### Specifications - **GPU:** NVIDIA Tesla T4 with 16GB VRAM - **CPU:** 4 vCPUs - **RAM:** 28GB - **Pricing:** $0.07/minute ### Example ```yaml jobs: ml-training: runs-on: gpu-t4-4-core steps: - uses: actions/checkout@v6 - name: Setup Python uses: actions/setup-python@v5 with: python-version: '3.11' - name: Install ML dependencies run: | pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 pip install -r requirements.txt - name: Train model run: python train.py --use-gpu - name: Run inference run: python inference.py ``` ### Use Cases - ML model training - GPU-accelerated testing - CUDA development - Image/video processing ### Common Validation Issues - Missing CUDA setup - Incorrect GPU driver versions - Not utilizing GPU in workloads (CPU fallback) - Missing GPU-specific dependencies --- ## M2 Pro macOS Runners (Larger Runners) ### Availability - **Generally available** with M2 Pro powered runners ### Labels ```yaml runs-on: macos-latest-xlarge # macOS 15, M2 Pro runs-on: macos-15-xlarge # macOS 15, M2 Pro runs-on: macos-14-xlarge # macOS 14, M2 Pro ``` ### Specifications - **CPU:** 5-core (vs 3-core standard) - **GPU:** 8-core with hardware acceleration (enabled by default) - **RAM:** 14GB - **Storage:** 14GB - **Performance:** Up to 15% faster than M1 runners - **Pricing:** $0.16/minute ### Example ```yaml jobs: ios-build: runs-on: macos-15-xlarge # M2 Pro with GPU acceleration steps: - uses: actions/checkout@v6 - name: Setup Xcode uses: maxim-lobanov/setup-xcode@v1 with: xcode-version: latest-stable - name: Build iOS app run: | xcodebuild -workspace App.xcworkspace \\ -scheme Production \\ -configuration Release \\ -archivePath build/App.xcarchive \\ archive - name: Run GPU-accelerated tests run: | # GPU acceleration automatically available xcodebuild test -scheme AppTests ``` ### Benefits - GPU hardware acceleration for Metal-based workloads - Improved build times for iOS/macOS apps - Better performance for Xcode builds - Native Apple Silicon performance --- ## Runner Selection Best Practices ### Decision Criteria 1. **Architecture compatibility:** ARM64 vs Intel x86_64 2. **Cost optimization:** Standard vs larger runners 3. **GPU requirements:** ML/AI workloads need GPU runners 4. **Operating system:** Latest versions recommended 5. **Deprecation timelines:** Avoid retired runners 6. **Public vs private repos:** ARM64 free only for public repos ### Validation Checklist ```yaml # Check these in your workflows: - [ ] Using latest runner versions (macos-15, windows-2025, ubuntu-latest) - [ ] Not using deprecated runners (macos-13) - [ ] Architecture-appropriate runners (ARM64 vs Intel) - [ ] GPU runners for ML workloads - [ ] Cost-effective runner selection - [ ] ARM64 compatibility tested (if using ARM64 runners) ``` ### Cost Comparison | Runner Type | Pricing | Best For | |-------------|---------|----------| | Standard (Linux/Windows) | Included | Most workloads | | Standard (macOS) | Included | iOS/macOS builds | | ARM64 (public repos) | **Free** | Multi-arch builds | | ARM64 (private repos) | Enterprise | ARM-native builds | | GPU (T4) | $0.07/min | ML/AI workloads | | M2 Pro (xlarge) | $0.16/min | Heavy iOS builds | --- ## Multi-Architecture Builds ### Example: Building for Multiple Architectures ```yaml jobs: build: strategy: matrix: include: - runner: ubuntu-latest arch: x64 - runner: ubuntu-latest-arm64 arch: arm64 runs-on: ${{ matrix.runner }} steps: - uses: actions/checkout@v6 - name: Build run: | echo "Building for ${{ matrix.arch }}" ./build.sh --arch ${{ matrix.arch }} - name: Upload artifact uses: actions/upload-artifact@v4 with: name: build-${{ matrix.arch }} path: dist/ ``` --- ## Self-Hosted Runner Configuration When using self-hosted runners, configure actionlint to recognize custom labels: ```yaml # .github/actionlint.yaml self-hosted-runner: labels: - my-custom-runner - gpu-runner - arm-runner - on-premises ``` This prevents actionlint from reporting unknown runner label errors. --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/runners) · https://claudary.paisolsolutions.com