A Step-by-Step Guide to Comparing ROCm 7.0.0 and 7.2.3 Performance on an AMD Radeon AI PRO R9700

Introduction

If you own a System76 Thelio Major workstation – or any machine equipped with an AMD Radeon AI PRO R9700 graphics card – you might be wondering whether updating your ROCm user-space components from the 7.0.0 release (late summer last year) to the latest 7.2.3 stable milestone delivers any meaningful performance gains. This guide walks you through the exact process I used to test and compare both ROCm versions on the same RDNA4 workstation GPU. By following these steps, you’ll be able to replicate the benchmarks and decide for yourself whether the upgrade is worth it.

What You Need

• Hardware: A workstation with an AMD Radeon AI PRO R9700 (RDNA4) graphics card. For reference, a System76 Thelio Major was used in the original tests.
• Operating System: A Linux distribution that supports ROCm 7.0.0 and 7.2.3 (e.g., Ubuntu 22.04 or 24.04 LTS).
• ROCm Installers: Download the ROCm 7.0.0 and ROCm 7.2.3 installation packages from AMD’s official repository.
• Benchmarking Tools: A consistent set of GPU benchmark applications. Examples: rocBLAS, rocFFT, rocPRIM, ROCm Bandwidth Test, or any suite that exercises compute, memory, and AI workloads.
• System Monitoring Tools: rocm-smi, radeontop, or similar utilities to log GPU clock speeds, temperatures, and power draw.
• Fresh System State: A clean installation of your OS (or a dedicated test partition) to avoid driver/library conflicts.
• Time: Approximately 2–3 hours for installation, benchmarking, and analysis.

Step-by-Step Instructions

Step 1: Prepare Your System

Begin by ensuring your Linux OS is fully updated and that you have a stable internet connection. It is highly recommended to start from a fresh system image or at least uninstall any previous ROCm installations (sudo apt purge rocm-*). Reboot after cleanup to reset kernel modules.

Step 2: Install ROCm 7.0.0

Follow the official ROCm 7.0.0 installation guide for your distribution. For Ubuntu, add the ROCm repository, install the rocm-dev meta-package, and ensure the amdgpu kernel driver is loaded. After installation, run sudo reboot and confirm that ROCm is recognized: rocminfo | grep 'Agent' should show the Radeon AI PRO R9700.

Step 3: Run Baseline Benchmarks

Pick a set of representative benchmarks (e.g., rocBLAS matrix multiply, rocFFT FFT, rocPRIM sort, and a memory bandwidth test). For each benchmark, record the execution time and throughput. Run each test at least three times and take the median value to reduce variance. Save the results in a log file (roc70_results.txt). Also log GPU metrics (clock frequency, temperature, power) using rocm-smi.

Step 4: Uninstall ROCm 7.0.0

Completely remove the ROCm 7.0.0 user-space components: sudo apt purge rocm-*. Also remove any leftover packages: sudo apt autoremove --purge. Reboot again to avoid library version conflicts.

Step 5: Install ROCm 7.2.3

Now install ROCm 7.2.3 following its installation instructions. The process is identical to Step 2, but use the repository and package versions for 7.2.3. After reboot, verify the new ROCm version: apt show rocm-dev | grep Version should display 7.2.3.

Step 6: Run the Identical Benchmarks

Use the exact same benchmark binaries and command-line arguments as in Step 3. Run the same tests (same number of iterations, same problem sizes) and again log the results to roc723_results.txt. Ensure the system load (other processes, background services) is similar to the baseline run.

Step 7: Compare and Analyze

Open both result files side by side. For each benchmark, calculate the percentage difference: ((723_result - 70_result) / 70_result) * 100. A positive number indicates a performance improvement with ROCm 7.2.3. Plot the metrics (e.g., using Python’s Matplotlib) to visualize gains. Pay attention to memory bandwidth and compute kernel changes – sometimes one version may be faster for certain operations but slower for others.

Step 8: Document Your Findings

Write a summary report that includes the hardware and software configuration, the exact versions used, the benchmark suite, and the raw results. Highlight any workloads that show significant improvement (or regression). This documentation will help you (or your team) decide whether to upgrade for your specific AI or HPC workloads.

Useful Tips

• Isolate the GPU. Close all unnecessary applications and disable desktop compositors to reduce interference.
• Use the same kernel module version. Both ROCm 7.0.0 and 7.2.3 work with the amdgpu kernel driver; ensure you are using the same kernel version for both tests (e.g., v6.5 or v6.8).
• Monitor thermal throttling. The Radeon AI PRO R9700 may throttle under prolonged load. Log temperatures and power draw; if throttling is severe, adjust fan curves or use a more aggressive cooling solution.
• Repeat benchmarks at least three times. GPU performance can vary slightly due to background system activities. Take median values for accuracy.
• Consider workload relevance. Not all workloads benefit equally. If your work uses specific ROCm libraries (e.g., MIOpen for ML), include those in your benchmark set.
• Keep the ROCm installation scripts. Save the exact commands used for installation – this makes it easy to reproduce the test environment later.
• Check for regression. If you notice a performance drop in a critical application, ROCm 7.2.3 might have introduced a change in a library you depend on. Roll back or file a bug report with AMD.
• Stay tuned for future releases. AMD often releases point releases with bug fixes and optimizations. You can recreate this guide for subsequent ROCm versions.

Note: The original investigation used a System76 Thelio Major with the AMD Radeon AI PRO R9700. The steps above are designed to be hardware-agnostic, but specific results will depend on your exact system configuration.