Why TileKernels is changing how we build LLM infrastructure

If you’ve spent any time optimizing LLM inference, you know the pain of writing custom CUDA kernels. You spend weeks chasing memory bandwidth bottlenecks, only to find that a minor architectural change renders your hard work obsolete. Most engineers get stuck in this cycle of manual optimization, but the release of TileKernels signals a shift toward a more agile, domain-specific approach to GPU performance.

Built on TileLang, this library provides a set of high-performance kernels that actually approach hardware limits without requiring you to write raw C++. It’s not just another wrapper; it’s a fundamental change in how we express compute-intensive operations like Mixture of Experts (MoE) routing and advanced quantization.

The shift from manual CUDA to TileLang

The real bottleneck in modern LLM training isn't just raw compute—it's the overhead of moving data between memory hierarchies. TileKernels addresses this by abstracting the complexity of tiling and memory management. Instead of manually managing shared memory buffers, you define the logic in Python, and the underlying compiler handles the heavy lifting.

Here’s where most people get tripped up: they assume that because it’s Python-based, it must be slower. That’s a misconception. Because TileLang compiles down to highly optimized GPU code, you get the performance of hand-written kernels with the maintainability of a high-level language. If you are currently struggling with custom MoE routing or fused quantization ops, this is the abstraction layer you’ve been waiting for.

Practical applications in MoE and Quantization

The library shines in its specialized modules. Whether you are dealing with per-token FP8 quantization or complex MoE token-to-expert mapping, the implementation is surprisingly clean.

• MoE Routing: Fused expansion and reduction operations that minimize kernel launches.
• Quantization: Native support for FP8, FP4, and E5M6 casting, often fused with SwiGLU to keep data in registers longer.
• Manifold HyperConnection: Specialized kernels for Sinkhorn normalization that are notoriously difficult to implement efficiently in standard PyTorch.

This next part matters more than it looks: the library includes torch.autograd.Function wrappers. This means you can drop these high-performance kernels directly into your existing training pipelines without rewriting your entire model architecture. You get the speed of a custom kernel with the ease of a standard PyTorch layer.

How to get started with TileKernels

If you want to see if this fits your stack, start by running the benchmarks. Don't just trust the theoretical performance; use the provided pytest utilities to measure the throughput on your specific hardware.

How do you know if your current kernels are underperforming? If your compute intensity is high but your memory bandwidth utilization is low, you are likely leaving performance on the table. Try running the benchmark suite with pytest --run-benchmark to see exactly where your current implementation stands against these optimized primitives.

This is the part nobody talks about: the maintenance burden of custom CUDA code is a silent killer of engineering velocity. By moving to a library like TileKernels, you aren't just gaining speed; you're gaining the ability to iterate on your model architecture without needing a PhD in GPU systems programming.

If you are ready to optimize your LLM infrastructure, install the development version and test it against your most compute-heavy layers. Share what you find in the comments—I’m curious to see how these kernels hold up against your specific hardware configurations.