Ridgeline: profiling LLM inference with the roofline model
Published on July 8, 2026, the text describes Ridgeline, a PyTorch-based profiler that applies the roofline model to analyze LLM inference workloads.
Source: Ridgeline: um profiler roofline para inferência de LLMs (github.com). Text prepared with AI from this source.
What happened and what to do
On July 8, 2026, the text introduced Ridgeline, a small roofline profiler built with PyTorch. The tool can help engineers analyze LLM inference workloads using a roofline model.
A company can use this kind of analysis to measure its inference workloads, identify bottlenecks, and track system behavior over time. The findings can inform performance and capacity improvements, using metrics and dashboards tailored to the company’s infrastructure and workloads.
How the consultancy can help
Wendelmaques can diagnose how to assess your inference workloads, define metrics, and scope a profiling and monitoring implementation. The engagement can include integration with existing infrastructure and operation of the solution.
Next step
Send a short description of your workload and performance challenge to receive a scoped proposal.
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