FlashAttention in CuPy/CUDA: implementation and performance
A project published on March 1, 2026 implements FlashAttention with tiling and online softmax, without materializing the NxN score matrix; it reports mathematical verification but no speedup over cuBLAS.
Source: Uma implementação de FlashAttention do zero em CuPy/CUDA (x.com). Text prepared with AI from this source.
What happened and what to do
The project, published on March 1, 2026, implements FlashAttention from scratch using CuPy and custom CUDA kernels. Its approach combines tiling and online softmax to avoid materializing the NxN score matrix. The author reports mathematical verification but no speedup over cuBLAS.
For a company evaluating custom kernels, this case highlights the importance of measuring performance against optimized libraries as well as validating correctness. A company could implement a reproducible benchmark, monitor latency and memory use across workloads, and determine whether there is an operational reason to maintain or optimize a custom implementation.
How the consultancy can help
Wendelmaques can assess the opportunity and performance requirements, scope benchmarking and validation, and implement measurement and monitoring pipelines or CUDA optimizations suited to the company’s infrastructure.
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