AI Detects Bottlenecks That Could Accelerate Linux Builds by Up to 70%

A Linux kernel developer turned to a language model to identify bottlenecks in the build process. Although the tool produced code that he described as terrible, a human review allowed for the preparation of 23 patches with improvements of up to 36% in full builds and 70% in incremental builds.

• Lorenzo Stoakes published a series of 23 patches to significantly speed up the Linux kernel build.
• The work targets single-threaded tasks in Kbuild, kallsyms, modpost, objtool, mksysmap, and the Rust build system.
• Tests report improvements of 36% in builds with all modules and up to 70% in incremental builds.

An AI-Assisted Investigation

A veteran Linux kernel developer used a large language model to locate obstacles that slow down the build process. The result was a series of 23 patches that, according to the described tests, could significantly accelerate various types of builds, although they still need to pass the review process before being incorporated into the main kernel.

Lorenzo Stoakes published the proposal on September 8, 2026, with a specific goal: to reduce the time the system remains stalled on single-threaded tasks. The idea is not to replace developers with artificial intelligence but to use it to inspect the process, find wait points, and suggest modifications that can then be technically audited.

However, the tool did not deliver a ready-to-implement result for the project. Stoakes explained that the model generated a lot of code, a considerable portion of which he described as terrible, so he had to extensively review and rewrite his proposals before turning them into a coherent series of patches.

The developer also noted that the model helped coordinate build runs, testing, debugging, and analysis. Still, he stated that he manually checked the correctness of both the builds and the running kernels, in addition to personally verifying the performance improvements.

The Problem of Single-Threaded Tasks

The kernel build combines numerous phases that can run across different processor cores, but some tasks remain limited to a single thread and create wait points. When one stage finishes before another, the available resources cannot always be fully utilized, meaning that the overall speed is conditioned by the least parallelized segments.

Stoakes' patches aim to better distribute several of these operations so that more work progresses simultaneously. The approach affects components of the build system such as Kbuild, kallsyms, modpost, objtool, and mksysmap, as well as the system used to compile Rust code within the kernel.

Kbuild coordinates much of the build process, while other tools generate information, verify objects, or prepare necessary elements for the final output. The proposal seeks to make these functions work more efficiently and reduce the intervals in which one phase is waiting for another to complete a sequential operation.

The scope of the series also shows why detecting bottlenecks can be more important than optimizing a single tool. A small improvement in several connected phases can yield a considerable saving in total time, especially when the system recompiles the kernel frequently during development and testing.

Reported Improvements in Different Builds

The cited results for a kernel compilation with all modules enabled point to an approximate 36% reduction in the time required. This measurement represents the broadest scenario in the series, as it includes a large number of components and allows observation of how optimizations behave when the process must build an extensive configuration.

Incremental builds, which only rebuild the parts affected by recent changes, could benefit even more. In that case, the mentioned tests report improvements of up to 70%, a significant difference for developers who repeat the cycle of modifying code, compiling, testing, and readjusting.

The figures do not represent a universal promise for every machine or kernel configuration, and the proposal itself includes benchmark results for different scenarios. Its main value lies in showing that single-thread bottlenecks still offer significant room for improving the compilation experience.

Human Audit and Next Steps

Stoakes noted the model's participation through an Assisted-by tag in each commit of the series. He also indicated that he heavily edited the commit messages, cover letter, and comments, a necessary task for other maintainers to understand the technical decisions and evaluate their consequences.

This oversight is particularly relevant in a project like the Linux kernel, where a performance change cannot be evaluated solely by compilation time. Patches must preserve system correctness, avoid errors in uncommon configurations, and maintain the stability of the code that ultimately runs on devices and servers.

The experience exposes a known tension in using language models for software engineering: they can quickly explore alternatives and detect patterns that are difficult to review manually, but their outputs may contain errors, poor structures, or decisions that do not meet project standards. In this case, the utility of AI appeared in research and analysis, while final validation remained under human responsibility.

The information was reported by Phoronix, which collected Stoakes' publication and the results of the patch series. The next step will be a technical review by the community and the corresponding maintainers, after which the work could reach the main kernel and accelerate compilations on various machines and configurations.

For now, the figures should be interpreted as results of a proposal under review and not as an improvement already available for all Linux distributions. If the patches retain their benefits without introducing correction issues, they could turn one of the most repetitive tasks in kernel development into a considerably less slow process.

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