A Coding Guide to NVIDIA’s Tile-Based GPU Programming: From cuTile and Triton Kernels to Flash Attention
NVIDIA's tile-based GPU programming isn't just faster—it's becoming the standard for inference optimization. Here's how to build with it.
Why it matters
As AI inference costs dominate operational budgets, mastering tile-based GPU programming (cuTile, Triton, Flash Attention) is becoming a core competency for ML engineers. This tutorial bridges the gap between NVIDIA's low-level optimization primitives and production implementation.
The key facts
11 to knowTutorial covers cuTile backend and Triton kernel fallbacks for hardware compatibility
Core tile programming model: batch data into tiles, load → compute → store
Implementations include vector addition, fused GELU, row-wise softmax, tiled matrix multiplication, flash attention
Validated against PyTorch baselines for correctness
Runnable Colab workflow across different GPU hardware
Addresses inference optimization at the kernel level—relevant to cost reduction strategies
Tutorial covers cuTile backend and Triton kernel implementation
Demonstrates tiled matrix multiplication and flash attention optimization
Addresses hardware fallback strategies (cuTile → Triton on standard Colab GPUs)
Practical Colab workflow for cross-hardware compatibility
Implements core tile-based operations: vector addition, fused GELU, row-wise softmax
Go to the source
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Publisher excerpt: In this tutorial, we explore NVIDIA tile-based GPU programming with TileGym, building a Colab workflow that runs across different hardware. We probe the CUDA environment, try the real cuTile backend, and fall back to Triton when standard Colab GPUs lack the cuTile stack. We learn the core tile…