timothy morano
January 14, 2026 21:15
NVIDIA releases a detailed cuTile Python tutorial for Blackwell GPUs demonstrating matrix multiplication that achieves over 90% of cuBLAS performance with simplified code.
NVIDIA has published a comprehensive developer guide for the cuTile Python framework, demonstrating how the new tile-based programming model can achieve over 90% of cuBLAS performance for matrix multiplication operations on Blackwell architecture GPUs.
This tutorial, written by NVIDIA Engineer Jinman Xie, walks developers through implementing high-performance matrix multiplication using the cuTile library, introduced in CUDA 13.1 in December 2025. Testing on an RTX 5080 showed that the cuTile implementation matches PyTorch’s cuBLAS-based operations over matrix sizes from 1024×1024 to 16384×16384.
Important changes for developers
This framework represents NVIDIA’s departure from traditional thread-level GPU programming. Instead of managing individual threads, developers now work with “tiles,” larger chunks of data that the compiler automatically optimizes for tensor core execution.
A complete cuTile matrix multiplication kernel requires approximately 30 lines of Python code. Key operations: Load tiles from matrices A and B, call ct.mma() for matrix multiply-accumulate (automatically calls tensor cores), and save the result. The framework handles thread synchronization and memory access patterns internally.
Current requirements limit adoption: CUDA 13.1 or higher, Blackwell architecture only (RTX 50 series, compute capabilities 10.x and 12.x), Python 3.10 or higher. NVIDIA has indicated that future CUDA releases will support a broader range of architectures.
Performance optimization details
This guide describes “swizzle” optimization, a technique that remaps block IDs to improve cache hit rates. NVIDIA’s example shows that swizzle memory accesses reduce total data load by 20% compared to linear row accesses, directly leading to increased throughput.
Tile size configuration is very important. For float16/bfloat16 operations, the tutorial recommends 128x256x64 tiles. For float32, 32×32×32. These are not universal, and the optimal parameters depend on matrix dimensions, GPU architecture, and available shared memory.
Market impact
NVIDIA stock was trading at $182.06 on January 14, down 2.02% on the day. As competition in the AI accelerator market intensifies, the company is pushing to simplify GPU programming.
The cuTile framework is important because the basis of virtually all neural network operations is matrix multiplication. Reducing the expertise barrier for writing high-performance GPU code could expand NVIDIA’s developer ecosystem, a key competitive moat as AMD and custom silicon vendors chase the AI training and inference market.
Complete code examples and benchmarks are available in NVIDIA’s TileGym repository. The autotuner tool automatically determines the optimal tile parameters for a given workload, addressing one of the key friction points in GPU kernel optimization.
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