Resource Library

The Ampere PMU Profiler (APP) is a Python-based tool designed to provide deep insight into the microarchitectural behavior of applications running on Ampere CPUs

The Ampere® System Profiler (ASP) is a python command line interface utility that uses a set of Linux profiling tools to gather system-level performance metrics while running applications of interest.

Ampere System Profiler (ASP) is a system-aware Linux profiling tool that correlates CPU, NUMA, I/O, network, scheduling, and power/thermal behavior to uncover root causes of performance symptoms with evidence captured in a single sample window.

Read this Redis workload brief benchmarking OCI Ampere® A4 Arm-based compute against similarly configured AMD E6 instances. Test results include up to 15% higher throughput and up to 25% better price-performance while meeting stringent latency SLAs.

This guide aims to help you configure the ultimate build server for Ampere's Arm-based architecture

Europe’s AI sovereignty depends on its regional cloud providers. Ampere helps regional providers deliver sovereign Arm-based infrastructure for modern workloads.

Explore why Apache Hadoop and Spark workloads can run faster on AmpereOne® M processors—optimized for cores, memory bandwidth, and power efficiency to improve big data and AI pipeline performance.

As enterprises deploy AI at scale, choosing the right model size and the right infrastructure can significantly reduce the energy footprint of AI workloads

Oracle expands its A4 portfolio with the new A4.Standard.Ax instance, combining AmpereOne M and Oracle Acceleron to power scalable AI inference and cloud workloads.

Agentic AI demands continuous, efficient infrastructure. Discover the hidden operational costs of scaling autonomy and how Ampere helps scale AI cost-effectively.

New AmpereOne® and AmpereOne® M instances across hyperscale and regional providers expand Arm-based cloud choice for European customers seeking scalable, locally operated infrastructure

In this article, we will look more closely at the operating system and Kubernetes configuration.