Key takeaways
GreenNode provides a hyperscale-class, high-performance AI cloud platform designed specifically for demanding enterprise workloads across training, inference, and data-intensive applications.
Its platform combines GPU-optimized compute, scalable clusters, and high-performance storage (including VAST-powered architecture) to remove bottlenecks and keep large AI workloads fast and reliable at scale.
This end-to-end AI infrastructure lets enterprises focus on building and deploying AI solutions while GreenNode manages performance, scalability, and operational complexity behind the scenes.

For years, global hyperscalers such as Google Cloud, AWS, and Azure have been considered the benchmark for cloud infrastructure performance. However, as demand for large-scale data processing, AI, and high-intensity enterprise systems continues to grow, the key question is no longer “cloud or on-prem,” but whether a cloud platform can sustain hyperscale-level performance in real-world, long-term operations.

GreenNode was built to address exactly this challenge. Rather than focusing solely on scalability or cost, the platform’s infrastructure is designed around sustained processing performance, especially for workloads such as AI/ML, big data analytics, high-performance databases, and large-scale parallel processing systems.

One of the core factors that allows GreenNode to approach hyperscaler standards lies in the hardware layer—specifically, the deployment of high-end 5th Gen Intel Xeon Scalable processors. Building on the segmentation foundation discussed in the previous article, GreenNode uses Intel Xeon Platinum processors to power instance families that deliver performance comparable to high-end hyperscaler configurations, while being optimized for long-running workloads and continuous processing demands.

Notably, GreenNode selects the Intel Xeon Platinum 8558P with a 2.7 GHz base frequency - an important factor in maintaining stable performance for high-intensity parallel processing tasks.

This article will provide a detailed comparison between the 5th Gen Intel Xeon Platinum configurations deployed on GreenNode and a representative hyperscale platform, Google Cloud Platform (GCP), highlighting key technical differences that directly impact real-world system performance.

CPU baseline specification comparison

On Google Cloud Platform, Intel Xeon Scalable Processor (Emerald Rapids) 5th generation machine series use the Intel Xeon Platinum 8581C. Below is a detailed comparison table:

Cloud Platform	CPU	Base frequency	Max Turbo frequency	VM purpose / Recommended workloads
GreenNode	Xeon Platinum 8558P	2.7 GHz	4.0 GHz	High-performance machine. Recommended workloads: Long-running AI/ML pipelines (data preprocessing, feature engineering, model training in coordination with GPUs) Large-scale batch processing and continuous ETL Big data analytics (Spark, Presto, ClickHouse, Hadoop jobs) High-performance databases with sustained query loads (OLAP, analytics databases) Backend systems with consistently high CPU utilization (ad-tech, recommendation systems, real-time scoring)
GCP C4	Xeon Platinum 8581C	2.3 GHz	4.0 GHz	General-purpose machine. Recommended workloads: High traffic web, app and ad servers Databases and caches Game servers Data analytics Media streaming and transcoding Network appliances CPU-based ML training and inference.
GCP N4	Xeon Platinum 8581C	2.1 GHz	3.3 GHz	General-purpose machine. Recommended workloads: Medium traffic web and application servers Containerized microservices Business intelligence applications Virtual desktops CRM applications Development & test environments Batch processing Storage and archive.
GCP M4	Xeon Platinum 8581C	2.1 GHz	4.0 GHz	Memory-optimized machine. Recommended workloads: OLAP and OLTP SAP workloads Memory intensive applications, such as genomic modeling and electronic design automation Small to medium in-memory databases such as SAP HANA High performance computing.
GCP A4 / GCP A3 Ultra	Xeon Platinum 8581C	2.1 GHz	4.0 GHz	Accelerator-optimized machine. Recommended workloads: Pre-training models Fine-tuning models Serving inference.

Overall, the comparison table shows that GreenNode positions its infrastructure toward high performance for workloads requiring sustained CPU loads, while the corresponding GCP series are designed with more variety, each optimized for different resource profiles and workload types.

Google Cloud tends to favor CPU configurations with lower base clocks but allows higher turbo frequencies for short bursts. GreenNode uses the Xeon Platinum 8558P with a 2.7 GHz base clock—17–40% higher than the base frequency of many GCP configurations.

Base vs. Turbo Frequency: A Difference in Design Philosophy

Characteristics	GCP Intel Xeon Platinum 8581C	GreenNode Intel Xeon Platinum 8558P
Low base clock	✔
High turbo boost	✔	✔
Optimized for short bursts	✔
Optimized for sustained load		✔

Why is base frequency important?

When evaluating CPU performance in the cloud, many people focus on turbo frequency—the figure that reflects a CPU’s ability to boost speed for short periods. However, for modern enterprise workloads, real-world performance is more often determined by base frequency.

Workloads such as multi-hour AI model training, large-scale batch processing, continuous ETL pipelines, and complex data warehouse queries share a common trait: the CPU must operate near 100% utilization for extended periods, rather than spiking briefly during short processing bursts.

In these scenarios, turbo frequency cannot be sustained due to thermal and power limits. Under prolonged full-load conditions, CPU clock speeds gradually stabilize at the base frequency—this is the performance level the system can maintain consistently over time.

Therefore, for long-running, high-intensity workloads, base frequency most accurately reflects a CPU’s real performance. Infrastructure with higher base frequencies can process more tasks per core within the same time frame, shortening job completion times and optimizing operational costs.

This is also a clear advantage of GreenNode, which uses CPU configurations with higher base clocks, making them well-suited for enterprise workloads that demand stable, sustained performance rather than short-term bursts.

Real-world impact on different workloads

Differences in basefrequency are not just technical specifications on paper—they directly affect the real-world performance of many enterprise workloads.

AI and ML training

In AI systems, the CPU is responsible for data preprocessing, feature engineering, and pipeline orchestration before data is sent to the GPU. These processes typically run continuously throughout the model training cycle. If the CPU becomes a bottleneck, GPUs can end up waiting idle, reducing the efficiency of these costly resources.

With a 2.7 GHz base frequency, the Intel Xeon Platinum 8558P enables faster and more stable CPU-side data pipeline processing, lowering the risk of bottlenecks and maintaining higher GPU utilization compared to configurations with only 2.1–2.3 GHz base frequencies.

The Intel Xeon Platinum 8558P (2.7 GHz base frequency) speeds up CPU pipelines, reduces bottlenecks, and keeps GPU utilization higher than CPUs with 2.1–2.3 GHz base frequency.

Big Data and Analytics

Data analytics systems such as Spark, Presto, ClickHouse, and BigQuery-like engines rely heavily on thread-level parallelism, memory bandwidth, and stable CPU clock speeds.

During long-running queries or jobs, CPUs in hyperscale environments often drop to base frequencies of 2.1–2.3 GHz. In contrast, configurations with a 2.7 GHz base frequency can sustain higher per-core processing performance. This results in shorter query times and higher throughput on the same number of cores, especially for large-scale data analytics tasks.

High-performance databases

OLAP and OLTP database systems run continuously under heavy workloads, where latency and per-core performance directly impact application experience. Under prolonged full-load conditions, turbo frequencies are no longer sustained, and the system depends on the CPU’s base clock.

A higher base clock helps reduce query tail latency and maintain more stable transaction throughput, particularly in systems that require fast response times and handle high transaction volumes.

Conclusion

Even when using the same 5th Gen Intel Xeon Platinum family, differences in CPU models and base clock configurations can lead to significant differences in real-world performance. Hyperscalers like Google Cloud typically optimize infrastructure for a broad mix of workloads at massive scale, while CPU configurations with higher base clocks are especially well-suited for continuous AI, Big Data, HPC, and database workloads.

As enterprises increasingly rely on long-running, high-intensity workloads, stable baseline performance becomes a key factor in both efficiency and operational cost. With a CPU platform designed to sustain real-world performance at hyperscale levels, GreenNode reinforces its position as a pioneer in cloud infrastructure innovation in Vietnam and across APAC—true to its positioning as a “High-performance AI cloud for digital-native businesses.”

Contact GreenNode now for free consultation on high-performance CPU platform instances.

GreenNode delivers hyperscale-class high-performance AI cloud platform for enterprise workloads