Timothy Morano
Jan 14, 2026 21:15
NVIDIA releases detailed cuTile Python tutorial for Blackwell GPUs, demonstrating matrix multiplication attaining over 90% of cuBLAS efficiency with simplified code.
NVIDIA has printed a complete developer information for its cuTile Python framework, demonstrating how the brand new tile-based programming mannequin can obtain over 90% of cuBLAS efficiency for matrix multiplication operations on Blackwell structure GPUs.
The tutorial, authored by NVIDIA engineer Jinman Xie, walks builders via implementing high-performance matrix multiplication utilizing the cuTile library launched with CUDA 13.1 in December 2025. Testing on an RTX 5080 confirmed the cuTile implementation matching PyTorch’s cuBLAS-backed operations throughout matrix sizes from 1024×1024 to 16384×16384.
What cuTile Modifications for Builders
The framework represents NVIDIA’s shift away from conventional thread-level GPU programming. As a substitute of managing particular person threads, builders now work with “tiles” – bigger information chunks that the compiler mechanically optimizes for tensor core execution.
An entire matrix multiplication kernel in cuTile requires roughly 30 strains of Python code. The important thing operations: load tiles from matrices A and B, name ct.mma() for matrix multiply-accumulate (which auto-invokes tensor cores), and retailer outcomes. The framework handles thread synchronization and reminiscence entry patterns internally.
Present necessities restrict adoption: CUDA 13.1 minimal, Blackwell structure solely (RTX 50 collection, compute functionality 10.x and 12.x), and Python 3.10+. NVIDIA signifies broader structure help will are available in future CUDA releases.
Efficiency Optimization Particulars
The information covers “swizzle” optimization – a method that remaps block IDs to enhance cache hit charges. NVIDIA’s instance exhibits swizzled reminiscence entry lowering whole information hundreds by 20% in comparison with linear row entry, translating on to throughput beneficial properties.
Tile dimension configuration issues considerably. For float16/bfloat16 operations, the tutorial recommends 128×256×64 tiles; for float32, 32×32×32. These aren’t common – optimum parameters rely on matrix dimensions, GPU structure, and out there shared reminiscence.
Market Implications
NVIDIA shares traded at $182.06 as of January 14, down 2.02% on the day. The corporate’s push to simplify GPU programming comes as competitors in AI accelerator markets intensifies.
The cuTile framework issues as a result of matrix multiplication underlies nearly all neural community operations. Decreasing the experience barrier for writing performant GPU code may increase NVIDIA’s developer ecosystem – a key aggressive moat as AMD and customized silicon distributors chase the AI coaching and inference markets.
Full code examples and benchmarks can be found in NVIDIA’s TileGym repository. The autotuner device can mechanically decide optimum tile parameters for particular workloads, addressing one of many essential friction factors in GPU kernel optimization.
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