---
title: "Measure the analyzer’s garbage-collection budget"
description: "Give a Go analyzer more memory headroom so it spends less time collecting garbage."
url: https://starsling.dev/ci-playbooks/techniques/measure-gc-memory-budget
canonicalUrl: https://starsling.dev/ci-playbooks/techniques/measure-gc-memory-budget
---

# Measure the analyzer’s garbage-collection budget

Give a Go analyzer more memory headroom so it spends less time collecting garbage.

## How to choose an approach

This technique currently has one documented company example. Validate its prerequisites, tradeoffs and original CI environment against your repository before adapting it.

- **[OpenAI](https://starsling.dev/ci-playbooks/openai#measure-gc-memory-budget)**: Use it when A Go-based analyzer spends substantial CPU collecting memory while the runner has headroom. Check before adopting: GOMEMLIMIT is a soft limit per Go process. Concurrent processes and native allocations can still exhaust the runner, so budget the memory actually free.

## Apply this technique

````text
Evaluate and apply this CI technique to my repository: Measure the analyzer’s garbage-collection budget.

Give a Go analyzer more memory headroom so it spends less time collecting garbage.

Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/measure-gc-memory-budget.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 OpenAI used this technique

Read what OpenAI changed and follow the original sources.

### OpenAI: Measure the analyzer’s garbage-collection budget

The Go SDK sets GOGC=400 for lint and derives GOMEMLIMIT from half of physical RAM, capped at 6 GiB.

Evidence: Source PR/commit evidence

Lint runs with GOGC=400 and a memory limit sized to the runner ([Source](https://github.com/openai/openai-go/pull/996))

Use it when: A Go-based analyzer spends substantial CPU collecting memory while the runner has headroom.

Before applying it: GOMEMLIMIT is a soft limit per Go process. Concurrent processes and native allocations can still exhaust the runner, so budget the memory actually free.

[Read more in the OpenAI playbook](https://starsling.dev/ci-playbooks/openai#measure-gc-memory-budget)

[Primary source](https://github.com/openai/openai-go/pull/996)

## 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)

## 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