Agents optimize GPU attention kernels
A September 28, 2026 NVIDIA post reports attention kernels up to 2.96 times faster than FlashKDA on B300, with one tenth the state error.
Source: Kernel Design Agents otimizam Kimi Delta Attention (nvlabs.github.io). Text prepared with AI from this source.
What happened and what to do
In a post published on September 28, 2026, NVIDIA describes using kernel design agents to generate and optimize attention kernels. It reports performance up to 2.96 times that of FlashKDA on B300, with one tenth the state error, and describes diagnosis using CUDA and specialized kernel languages.
A company running attention workloads on GPUs can assess whether specialized kernels could improve performance or accuracy. A practical effort starts by measuring the existing workload, setting error and latency criteria, and testing alternatives in a controlled environment, with monitoring to compare results and detect regressions.
How the consultancy can help
Wendelmaques can diagnose the attention workload and GPU infrastructure, define evaluation metrics and scope, and implement tests, monitoring, and kernel optimizations suited to the company’s environment.
Next step
Send a short description of your GPU workload, performance goals, and technical constraints to receive a scoped proposal.
Let's assess your project
I reply with scope and a proposal.