Jan 14, 2026 21:15
NVIDIA releases detailed cuTile Python tutorial for Blackwell GPUs, demonstrating matrix multiplication achieving over 90% of cuBLAS performance with simplified code.
NVIDIA has published a comprehensive developer guide for its 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.
The tutorial, authored by NVIDIA engineer Jinman Xie, walks developers through implementing high-performance matrix multiplication using the cuTile library introduced with CUDA 13.1 in December 2025. Testing on an RTX 5080 showed the cuTile implementation matching PyTorch’s cuBLAS-backed operations across matrix sizes from 1024×1024 to 16384×16384.
What cuTile Changes for Developers
The framework represents NVIDIA’s shift away from traditional thread-level GPU programming. Instead of managing individual threads, developers now work with “tiles” – larger data chunks that the compiler automatically optimizes for tensor core execution.
A complete matrix multiplication kernel in cuTile requires roughly 30 lines of Python code. The key operations: load tiles from matrices A and B, call ct.mma() for matrix multiply-accumulate (which auto-invokes tensor cores), and store results. The framework handles thread synchronization and memory access patterns internally.
Current requirements limit adoption: CUDA 13.1 minimum, Blackwell architecture only (RTX 50 series, compute capability 10.x and 12.x), and Python 3.10+. NVIDIA indicates broader architecture support will come in future CUDA releases.
Performance Optimization Details
The guide covers “swizzle” optimization – a technique that remaps block IDs to improve cache hit rates. NVIDIA’s example shows swizzled memory access reducing total data loads by 20% compared to linear row access, translating directly to throughput gains.
Tile size configuration matters significantly. For float16/bfloat16 operations, the tutorial recommends 128×256×64 tiles; for float32, 32×32×32. These aren’t universal – optimal parameters depend on matrix dimensions, GPU architecture, and available shared memory.
Market Implications
NVIDIA shares traded at $182.06 as of January 14, down 2.02% on the day. The company’s push to simplify GPU programming comes as competition in AI accelerator markets intensifies.
The cuTile framework matters because matrix multiplication underlies virtually all neural network operations. Reducing the expertise barrier for writing performant GPU code could expand NVIDIA’s developer ecosystem – a key competitive moat as AMD and custom silicon vendors chase the AI training and inference markets.
Full code examples and benchmarks are available in NVIDIA’s TileGym repository. The autotuner tool can automatically determine optimal tile parameters for specific workloads, addressing one of the main friction points in GPU kernel optimization.
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