Last Thursday, our team participated in a deep technical session that explored the capabilities of AWS's Graviton family of processors. Over the years, Graviton has become a pivotal CPU architecture for companies seeking to cut cloud costs while maintaining high levels of performance. With each new generation, AWS has pushed the envelope in terms of what's possible in cloud infrastructure, and this session shed light on Graviton's potential to transform how businesses operate in the cloud.
AWS first introduced the Graviton processor to bring significant cost reductions to cloud operations, offering up to 45% savings compared to Intel-based instances. Built on ARM architecture, Graviton was particularly effective for Linux workloads, where it reduced CPU costs without sacrificing operational efficiency.
Graviton 2 followed with a notable 40% performance increase, bringing improved memory access and core efficiency. This made Graviton 2 a strong choice for a variety of workloads, including those requiring parallel data processing and large-scale computation.
When AWS released Graviton 3, users saw an additional 25% performance boost, particularly in floating-point calculations. This upgrade further solidified Graviton's status as a top-tier option for compute-intensive tasks such as AI training and big data analytics.
Most recently, Graviton 4 was launched, offering a 50% increase in core scaling compared to Graviton 3, with up to 192 cores for the largest R8g instance type with dual sockets. This makes Graviton 4 a powerful architecture for workloads that demand high CPU throughput, such as parallel computing. Graviton 4 not only provides a performance boost but also allows businesses to scale more efficiently than ever before.
One of the key advantages of Graviton processors is their ability to utilize full dedicated cores rather than relying on hyper-threading, which is common with Intel and AMD processors. Hyper-threading simulates multiple threads per core, but under heavy load, this can cause performance bottlenecks, with CPU utilization spiking prematurely.
Graviton’s architecture eliminates this problem by using dedicated cores, which ensures consistent and predictable performance even during high-demand workloads. In the technical session, benchmark tests showed Graviton’s superiority when handling millions of queue requests. In a certain model tested, the Intel instances began to fail at around 120 requests per second, while the Graviton instances managed up to 250 requests per second without crashing. This makes Graviton not only more powerful but also far more reliable for mission-critical applications.
During the session, we discussed how Graviton offers more than just raw performance—it also delivers substantial cost savings. Businesses that have switched from Intel or AMD to Graviton-based instances, such as C7g, have reported cost reductions of around 20%. The savings come from two main areas: lower per-instance costs and the ability to run more workloads on fewer machines.
One example shared during the session involved a customer who switched from AMD’s C5A instance to Graviton’s C7g, resulting in a 24% cost reduction. This customer was able to consolidate workloads, reducing the overall number of instances required while simultaneously improving performance.
In addition to cost savings, Graviton processors also offer reduced latency and faster request handling, which is crucial for organizations scaling their operations. Graviton has also been integrated into AWS’s managed services, including RDS, Aurora, DynamoDB, and ElasticCache. This means customers using these services can benefit from the increased efficiency and lower costs associated with Graviton processors without needing to modify their applications.
A reminder is that the Graviton instances are also available at the AWS’s Spot market, where businesses can take advantage of unused EC2 capacity at reduced prices. This creates additional savings for companies with flexible workload requirements.
AWS isn’t stopping at CPU performance with Graviton. During the session, we also explored how Graviton can work its way into AI and machine learning workloads, which traditionally rely on GPU processing. With frameworks like TensorFlow, businesses can run machine learning models directly on Graviton processors, further reducing the need for expensive GPUs.
For organizations that rely on machine learning, this shift opens up exciting possibilities. Graviton’s efficient CPU architecture can now handle workloads previously reserved for GPUs, offering a cost-effective solution for medium to large-scale AI applications.
The insights from the technical session clearly highlighted AWS’s commitment to developing high-performance, cost-efficient processors that meet the growing demands of cloud infrastructure. From Graviton 1 to Graviton 4, each generation has brought improvements that enable businesses to optimize their workloads, reduce operational costs, and scale efficiently.
For any organization looking to modernize its cloud infrastructure, Graviton offers a clear path to doing so. Its unique combination of full-core utilization, improved performance with each iteration, and lower costs makes it an ideal choice for companies that demand both speed and efficiency.
If you're ready to take advantage of the impressive performance and cost savings that Graviton can offer, Cloudride is here to assist. Our team of AWS experts specializes in optimizing cloud infrastructures to harness the full capabilities of Graviton processors, ensuring your workloads run more efficiently and cost-effectively. Whether you're looking to transition from your current instances or want to enhance your cloud environment, we provide tailored solutions that meet your specific business needs.
Reach out to us today to learn how Cloudride can help you maximize the benefits of AWS Graviton and elevate your cloud infrastructure to the next level.