With the rollout of Graviton5, AWS is claiming a significant jump in price-performance. I’m currently running a large cluster of x86 C6i instances for our Python-based microservices. Has anyone done a real-world benchmark on Graviton5? I’m worried about the time it takes to rebuild all our Docker images for ARM64 and if the actual cost savings justify the engineering hours.
3 answers
We recently moved about 40% of our fleet to Graviton5, and the results were better than expected. For compute-heavy Python tasks, we saw nearly a 35% improvement in latency and a 20% reduction in cost. The migration isn't as scary as it sounds if you are already using multi-arch Docker builds. We used AWS CodeBuild to generate images for both x86 and ARM64 simultaneously. The real "gotcha" is checking your C-extensions in third-party libraries; some older Python packages don't have pre-compiled ARM wheels, which can slow down your build pipeline. Overall, for high-traffic APIs, the ROI is definitely there.
When you ran your benchmarks, did you notice if the memory bandwidth improvements in Graviton5 helped specifically with your data-intensive tasks or just the raw CPU cycles?
Graviton5 is a game changer for sustainability too. If your company has green initiatives, these chips use much less power for the same workload compared to traditional x86.
Great point, Laura. We used the AWS Customer Carbon Footprint Tool to show our stakeholders that moving to Graviton actually helped us hit our quarterly ESG goals early.
We actually saw the biggest gain in our data-shuffling operations. The increased memory bandwidth in the 5th gen chips reduced our "I/O Wait" times significantly. If your Python services are doing a lot of JSON parsing or data serialization, the memory improvements are actually more noticeable than the raw clock speed increases compared to the previous Graviton4 or the Intel equivalents.