Input padding to reuse variable-size CUDA graphs
In a post published on February 26, 2026, the author recommends padding inputs to nearby size ranges and creating a family of CUDA graphs to reduce recompilations in inference workloads with variable sequence lengths.
Source: Padding de inputs para reutilizar CUDA graphs de tamanhos variáveis (x.com). Text prepared with AI from this source.
What happened and what to do
In a post dated February 26, 2026, the author proposes setting a maximum input size, padding each input to the nearest range, and maintaining a family of CUDA graphs for different sequence lengths. According to the post, this strategy avoids recompilations in inference workloads with variable inputs.
A company can assess the distribution of its input lengths and implement padding and graph selection suited to its workload. Metrics for latency, memory use, and recompilations can help validate the configuration and guide adjustments, rather than assuming one range fits every case.
How the consultancy can help
Wendelmaques can diagnose the workload and its exposure to recompilations, define a range strategy and scope, then implement and operate the inference pipeline with latency and memory monitoring.
Next step
Send a short description of your workload to receive a scoped proposal to assess and implement this strategy.
Let's assess your project
I reply with scope and a proposal.