--- title: "Persist compiler output between runs" description: "Restore compatible compiler outputs and intermediate objects instead of recompiling unchanged inputs." url: https://starsling.dev/ci-playbooks/techniques/persist-compiler-cache canonicalUrl: https://starsling.dev/ci-playbooks/techniques/persist-compiler-cache --- # Persist compiler output between runs Restore compatible compiler outputs and intermediate objects instead of recompiling unchanged inputs. ## How to choose an approach Compare the examples by repository setup, prerequisites, and risks before adopting one. Each company implemented this in its own environment; measure each change against your own baseline. - **[OpenAI](https://starsling.dev/ci-playbooks/openai#cache-cross-compile-objects)**: Use it when One producer job compiles the same Rust or C++ inputs on every run before handing test binaries to native runners. Check before adopting: Compiler caches need the toolchain, target and flags in their identity, and trusted writers only. Cached compilation leaves native test execution in place. - **[Ramp](https://starsling.dev/ci-playbooks/ramp#cache-the-compiler)**: Use it when A build job compiles the same native outputs from scratch on every run, and release builds run on tags. Check before adopting: A new tag build starts from the default branch's caches, so the warming run on main must use the same cache key prefix as the tag build. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Persist compiler output between runs. Restore compatible compiler outputs and intermediate objects instead of recompiling unchanged inputs. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/persist-compiler-cache.md Inspect this repository’s CI workflows, build and test commands, dependencies, and recent run timings. Identify where this technique applies and explain the expected benefit using this repository’s evidence. Measure a baseline from recent comparable runs: elapsed time, queue time, runner minutes and failures where available. If data is unavailable, say what cannot be measured. Compare relevant company approaches, checking prerequisites, platform differences and tradeoffs. Rank applicable options by evidence, fit and measurable opportunity; do not present company results as predictions for this repo. Recommend the smallest safe change, show what checks and fallback paths remain, and ASK ME WHICH CHANGE TO APPROVE BEFORE EDITING ANY FILES. Only after approval, work on a new feature branch, make one reversible change, and open a reviewable pull request. Never merge or push to a default or protected branch. Keep required checks, test coverage, deployment and release behavior intact. Validate the change and compare equivalent runs where possible; report limitations and what should be monitored after merge. ```` ## How companies used this technique Read what each company changed and follow the original sources. ### OpenAI: Cache the cross-compile producer’s objects Codex's archive refactor moved the Windows ARM64 build onto a Windows x64 producer and turned on sccache for it, keying the producer's caches by the archive runner so the cross-compile reuses the Windows x64 cache. Evidence: Source PR/commit evidence sccache on for the Windows ARM64 archive producer ([Source](https://github.com/openai/codex/pull/23358)) Use it when: One producer job compiles the same Rust or C++ inputs on every run before handing test binaries to native runners. Before applying it: Compiler caches need the toolchain, target and flags in their identity, and trusted writers only. Cached compilation leaves native test execution in place. [Read more in the OpenAI playbook](https://starsling.dev/ci-playbooks/openai#cache-cross-compile-objects) [Primary source](https://github.com/openai/codex/pull/23358) ### Ramp: Persist the compiler cache and warm it from the default branch Ramp's CLI release workflow compiles the Python CLI to native binaries with Nuitka on every version tag. Ramp rooted Nuitka's caches (ccache on Linux and macOS, clcache on Windows) in the workspace through NUITKA_CACHE_DIR and saved them with actions/cache, keyed on the lockfile with restore prefixes. Caches are scoped per ref, so a new release tag starts from the default branch's caches and shares nothing with other tags; the workflow therefore also runs on every push to main to warm the cache; those runs build but skip packaging, artifact upload and the release jobs. A rerun of the same tag can also reuse that tag's own cache. Evidence: Source PR/commit evidence In the commit Release v0.2.26, the release workflow restores the Nuitka compiler cache on every build and warms it on each push to main. ([Source](https://github.com/ramp-public/ramp-cli/commit/4fec456d52fd03f85e1b96c94e075d22b5306c51)) The commit's workflow comments describe the change; the quote is from the diff. GitHub's cache rules let a run restore caches from its own ref and the default branch, so the quoted comment is narrower than that rule. Use it when: A build job compiles the same native outputs from scratch on every run, and release builds run on tags. Before applying it: A new tag build starts from the default branch's caches, so the warming run on main must use the same cache key prefix as the tag build. [Read more in the Ramp playbook](https://starsling.dev/ci-playbooks/ramp#cache-the-compiler) [Primary source](https://github.com/ramp-public/ramp-cli/commit/4fec456d52fd03f85e1b96c94e075d22b5306c51) ## CI optimization is continuous work. Apply this technique yourself, or use StarSling Runners for faster GitHub Actions and optional agent-driven optimization PRs, which teams on eligible paid plans can turn on. [Get self-improving CI](https://starsling.dev/products/runners) ## Related techniques ### [Cache package downloads and dependency stores](https://starsling.dev/ci-playbooks/techniques/reuse-package-downloads) Restore package-manager downloads or dependency stores while keeping a locked install that validates the requested dependency graph. 7 implementations · 6 companies (DoorDash, Linear, Shopify, Spotify, Stripe, Uber) ### [Bake stable dependencies into runner images](https://starsling.dev/ci-playbooks/techniques/prebuild-runner-images) Prepare versioned system dependencies and build downloads once in the runner image instead of installing them on every job. 2 implementations · 2 companies (Anthropic, Linear) ### [Cache the directories the build actually uses](https://starsling.dev/ci-playbooks/techniques/cache-real-output-paths) Align restored and saved cache paths with the tool’s real output directories so the next run can use the cached work. 2 implementations · 2 companies (OpenAI, Uber) ## Company Playbooks ### [Anthropic](https://starsling.dev/ci-playbooks/anthropic) Run only affected tests ### [DoorDash](https://starsling.dev/ci-playbooks/doordash) Run build and test in parallel, sharded across CI agents ### [Linear](https://starsling.dev/ci-playbooks/linear) Benchmark the same job on faster hardware ### [Mastra](https://starsling.dev/ci-playbooks/mastra) StarSling runners for the slowest test workflow ### [OpenAI](https://starsling.dev/ci-playbooks/openai) Rank CI fixes by the minutes they save ### [Ramp](https://starsling.dev/ci-playbooks/ramp) Move a CPU-bound test suite to a faster CPU ### [Shopify](https://starsling.dev/ci-playbooks/shopify) Let an agent iterate against one CI metric ### [Spotify](https://starsling.dev/ci-playbooks/spotify) Build arm64 on native arm64 runners ### [Stripe](https://starsling.dev/ci-playbooks/stripe) Run only the tests a change can reach ### [Uber](https://starsling.dev/ci-playbooks/uber) Build the merge-queue speculations most likely to be needed ## Related guides Implementation guides for this technique. ### [Cache workflow dependencies](https://starsling.dev/best-practices/github-actions/cache-dependencies) Choose dependency cache paths and keys for your package manager. ### [Cut repeated test setup](https://starsling.dev/best-practices/github-actions/cut-repeated-test-setup) Share expensive setup at the scope your tests need. ### [Share setup steps across jobs](https://starsling.dev/github-actions/optimizations/avoid-duplicated-setup) Centralize repeated setup definitions and remove duplicate or unused steps. ### [Optimize npm installs](https://starsling.dev/github-actions/optimizations/optimize-npm-install) Cache npm dependencies and large downloads and remove unused tool installs. ### [Optimize pnpm installs](https://starsling.dev/github-actions/optimizations/optimize-pnpm-install) Set up the pnpm store cache in the right order and scope recursive commands.