How to speed up the Rust compiler in September 2026

Overall improvements and focus areas

• Commenters highlight recent ~4.5% mean wall-time reduction across hundreds of benchmarks as impressive.
• Many note that big wins come from algorithmic changes (e.g., better control-flow traversal massively reducing certain analysis calls), not micro-optimizing hot loops.
• New work on Polonius/trait solving and “only doing expensive analysis when needed” is seen as pushing Rust towards more targeted, demand-driven compilation.

Why rustc is slow (and how it compares)

• No single cause: cited factors include generics and monomorphization, iterator-heavy idioms, complex trait resolution and type inference, borrow checking, heavy LLVM optimizations, and large debug info.
• Multiple posts stress that idiomatic Rust inherently uses many generics (e.g., Option, Result, iterator chains), causing lots of IR for LLVM and more work for the linker.
• Some argue trait solving is not usually the main bottleneck; the sheer IR volume plus LLVM and linking dominate.
• Compared with C, Rust and C++ are seen as broadly similar in speed and both far slower than C; Go and Zig are noted as deliberately trading features and optimizations for fast compilation.
• Others argue Rust could have been designed closer to the Pareto frontier on both safety/performance and compile time, but compile speed was secondary during language design.

Tooling, backends, and linking

• Linking is frequently a bottleneck in symbol-heavy projects, even for debug builds; newer linkers (e.g., wild) improve things but require explicit setup.
• Cranelift is praised for very fast codegen but criticized for poor debugger support and missing intrinsics; some doubt it will become a stable backend.
• Macro and proc-macro expansion are identified as major costs. Experimental proc-macro caching (-Zcache-proc-macros) shows double-digit speedups on macro-heavy crates, but stabilization and opt-in semantics remain open questions.

Project structure, caching, and disk usage

• Splitting large codebases into multiple crates improves parallelism and reduces per-edit recompilation, but does not fix linking time.
• Global and shared caches (e.g., sccache, newer tools like kache) are strongly recommended; without them, many parallel sandboxes recompile the same dependencies.
• Large Rust projects can accumulate huge target directories and incremental artifacts (hundreds of GB), forcing periodic manual cleanup; how to reclaim cache space safely is described as an unsolved or very hard problem.
• Some suggest wrappers or global build locks to avoid CPU oversubscription, which can otherwise make builds substantially slower.

Parallel and speculative compilation ideas

• There is active work on a parallel frontend and TPDE (type-driven partial compilation) that could bring larger speedups.
• One commenter describes an experimental branch that emits metadata earlier so dependent crates can start sooner, yielding substantial wall-time reductions on deeply nested projects; handling error reporting and speculative “wasted” work is an open challenge.

Rust, agents, and iteration speed

• Several posts frame Rust as a strong fit for “agentic AI” outputs: native, low-defect, readable for humans, and good for serious desktop apps.
• Others report abandoning Rust for Go or other languages because slow compiles, heavy dependencies, and large disk usage make multi-agent workflows painful.
• Some argue that for large organizations and realistic projects, Rust and Go teams are roughly equally productive, so focusing on compile time alone is misleading; others counter that faster compiles could still significantly improve productivity, especially for AI-driven iteration.

Language design trade-offs and alternatives

• Go and Zig are cited as examples where compiler performance shaped language design; Rust is described as “wanting to have its cake and eat it too” with many features and checks.
• Some point to other ecosystems (D, OCaml, Haskell, Ada, Delphi, C++) where fast compilers, interpreters, REPLs, or hot reload tooling exist, and argue Rust’s main gap is ecosystem/tooling rather than inherent language cost.
• The orphan rule is called a major pain point but also a compile-time coherence win; there is interest in relaxing it in workspaces, though this might complicate compilation and performance.
• A few see Nim or other “middle-ground” languages as more balanced options between Rust’s complexity and Go/Zig’s simplicity.

Funding, LLM policies, and influence

• Corporate donations to Rust infrastructure are generally welcomed as enabling sustained work on performance; some suggest that being able to show a 5% reduction in developer wait time justifies such investment.
• Others worry that large donors can subtly steer priorities even without formal control, by funding work that serves their needs first.
• The Rust project’s LLM policy forbids submitting LLM-generated code directly but allows using LLMs for analysis and assistance; at least one compiler engineer explicitly notes using LLMs this way.
• There is debate about whether LLM-written code or large free token grants from AI companies should play a larger role in speeding up compiler development.

Skepticism about Rust’s value in the AI era

• Some participants question why Rust is attractive at all in an AI-heavy world: they argue that raw runtime performance matters less than before, while Rust’s verbosity, complex types, and slow compile times waste both human and AI resources.
• Static typing (and by analogy Rust’s safety guarantees) is portrayed by these skeptics as “training wheels” that LLMs don’t need, similar to perceived overuse of TypeScript leading to over-engineered, ceremony-heavy codebases.
• Others strongly disagree, emphasizing that Rust’s guarantees and runtime efficiency are exactly what is needed for robust, low-defect systems, even if that means paying more at compile time.