MinIO’s S3 over RDMA Initiative: Setting New Standards in Object Storage for High-Speed AI Data Infrastructure

As the demands of AI and machine learning continue to accelerate, data center networking is evolving rapidly to keep pace. For many enterprises, 400GbE and even 800GbE are becoming standard choices, driven by the need for high-speed, low-latency data transfer for AI workloads that are both data-intensive and time-sensitive. AI models for tasks like large language processing, real-time analytics, and computer vision require vast amounts of data to be processed and moved between storage and compute nodes almost instantaneously. Traditional network speeds are simply not sufficient to handle the data throughput these workloads demand.

This shift toward 400GbE/800GbE is a natural evolution to support AI applications that rely on massive, distributed datasets, typically processed across clusters of GPUs or specialized accelerators. However, as network speeds increase, conventional protocols such as TCP/IP struggle to maintain efficiency, creating bottlenecks due to high CPU overhead and latency. 

By aligning its S3 capabilities with RDMA, MinIO is pioneering new ways to meet the performance and scalability requirements of modern AI workloads, while also positioning customers for seamless transitions to even higher-speed network standards. This forward-looking support for S3 over RDMA extends MinIO’s leadership position for enterprises building AI-ready data infrastructures optimized for the future. The S3 over RDMA capability is available in the new AIStor for customers under private preview.

What is RDMA?

Remote Direct Memory Access (RDMA) allows data to be moved directly between the memory of two systems, bypassing the CPU, operating system, and TCP/IP stack. This direct memory access reduces the overhead and delays associated with CPU and OS handling of data, making RDMA particularly valuable for low-latency, high-throughput networking.

Why RDMA becomes more relevant as we move towards 800GbE and beyond networking for AI Infrastructure

As the need for faster data access intensifies, 400GbE/800GbE networking is set to become the backbone of AI data infrastructures. While TCP/IP have supported Ethernet’s growth over the years, it struggles with the requirements of ultra-high-speed networks and here’s why:

• CPU Bottlenecks: TCP/IP relies heavily on the CPU for processing tasks like packet handling, reassembly, and flow control. At 800GbE, the sheer volume and speed of packets can overwhelm the CPU, creating a performance bottleneck.

• Latency and Jitter: TCP/IP processes data through multiple layers (application, transport, network, link), adding latency. Buffering, retransmission, and packet reassembly further increase latency and jitter, which are magnified at higher speeds.

• Memory Bandwidth Constraints: TCP/IP transfers data between user and kernel space, adding multiple memory copies. At 800GbE, this strains memory bandwidth, further slowing performance.

RDMA has become a crucial technology for handling the massive data flows and minimizing CPU overhead at these speeds. RDMA tackles TCP/IP’s limitations in high-speed networking through:

• Direct Memory Access: RDMA bypasses the kernel and CPU, reducing latency by allowing memory-to-memory data transfers.
• Zero-Copy Data Transfer: Data moves directly from one application’s memory to another’s without intermediate buffering, improving efficiency.
• CPU Offloading: RDMA offloads network processing to the NIC, freeing CPU resources.
• Efficient Flow Control: RDMA’s NIC-based flow control is faster and uses fewer CPU cycles than TCP’s congestion control, allowing for more stable high-speed performance.

The Ethernet Imperative

RDMA’s advantages have traditionally been limited to high-performance computing (HPC) environments using InfiniBand and it has long been favored for low-latency, high-throughput applications. Ethernet, however, has emerged as the preferred choice for AI and other data-intensive workloads and here’s why: 

• Cost and Ubiquity: Ethernet is cost-effective and widely compatible, whereas InfiniBand requires specialized hardware and expertise. Ethernet's universal compatibility across platforms makes it easier to implement, particularly with the integration of RoCE.

• RoCE Standardization: RDMA over Converged Ethernet (RoCE) brings RDMA’s benefits to Ethernet, supporting low-latency, high-throughput data transfer on a familiar, scalable infrastructure.

• Versatility: Unlike InfiniBand, which is typically reserved for specialized environments, Ethernet supports a range of workloads on a single network infrastructure. For AI and data analytics environments, Ethernet provides flexibility without the need for separate network architectures.

For companies looking to future-proof their AI data infrastructure, Ethernet—especially with RoCE for RDMA support—is the logical choice, balancing performance with cost-effectiveness.

S3 over RDMA: Future-Proofing AI Deployments for Tomorrow’s Network Standards

As AI network infrastructure evolves, MinIO’s integration of S3 over RDMA provides the ultra-low latency and high throughput necessary for AI workloads that require fast, reliable data access, especially during model training and inference. This helps in:

• Reduced Latency:  With RDMA’s memory-to-memory data transfer, S3 GET and PUT requests are processed with minimal delay, enabling faster data retrieval in AI training and analytics workflows.
• Improved Throughput:  RDMA allows MinIO to handle more parallel data transfers without CPU bottlenecks, which is critical in GPU-heavy AI environments.
• Efficiency Gains:  By offloading data handling to RDMA-enabled NICs, MinIO reduces CPU usage, allowing organizations to focus more resources on AI model training and analysis.
• Compatibility with Future Ethernet Standards:  RDMA provides a pathway to terabit Ethernet speeds, making MinIO’s S3 solution scalable as networking technology advances.
• Cost-Efficiency: By reducing CPU dependency,  RDMA lowers energy and operational costs, particularly valuable as organizations scale their data infrastructure.

With S3 over RDMA, MinIO offers a robust, future-ready object storage platform that aligns with the highest standards in data center networking.

Conclusion

MinIO’s move to support S3 over RDMA is a forward-thinking response to the demands of modern, high-speed networking environments. By leveraging RDMA’s low-latency, high-throughput capabilities within the familiar S3 framework, MinIO enables customers to take full advantage of their 400GbE and 800GbE Ethernet investments, providing a fast, scalable, and efficient storage solution. For enterprises looking to future-proof their AI and data-intensive workloads, MinIO’s S3 over RDMA ensures their infrastructure can meet tomorrow’s demands today, positioning MinIO as the definitive choice for high-performance object storage in the age of next-gen networking.