Mojo for AI Kernels: 10 Patterns that Beat Python

Practical patterns to write near-metal AI kernels in Mojo — without giving up Pythonic ergonomics.

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Ten proven Mojo patterns — tiling, fusion, SIMD, static shapes, and more — that deliver massive speedups over pure Python for AI kernels.

Let’s be real: pure Python loops fold under real-time inference and training loads. Mojo flips the script — Pythonic where you type, systems-level where it counts. Below are ten patterns I keep reaching for when I want CUDA-adjacent performance on CPUs and GPUs, while staying productive.

1) Static Shapes & Strong Types First

Dynamic shapes are convenient; stable shapes are fast. In Mojo, pin down element types and dimensions early. The compiler can then inline, unroll, and vectorize with confidence.

struct Tensor[Shape: Int, T: DType]:
    ptr: ptr T
    len: Int = Shape

@inline
fn relu_inplace(x: Tensor[1024, Float32]):
    for i in range(0, x.len):
        let v = x.ptr[i]
        x.ptr[i] = max(v, 0.0)

Why it beats Python: The compiler sees fixed length and type, removes bounds checks, and generates tight loops instead of interpreter overhead.

2) Cache-Aware Tiling