Oracle Acceleron Adds Front-End RDMA and Multiplanar Failover for AI

Oracle announced new Oracle Acceleron cloud networking capabilities for AI workloads at Oracle AI World in Las Vegas on October 27, 2026, introducing front-end RDMA connectivity, expanded NVMe block-storage options that Oracle said improve performance up to 3x, and a collective communications library for its multi-planar networks.

Oracle Acceleron is Oracle Cloud Infrastructure’s suite of networking and infrastructure technologies for running demanding AI workloads reliably, securely, and at scale. It combines dedicated network fabrics, more direct data paths, and a converged NIC, with host-level zero-trust routing and multi-planar designs for security and resilience. The new capabilities span three areas: AI data movement, distributed AI communication, and secure multi-tenant AI infrastructure.

“When thousands of accelerators are working together, one congested or failed path can have consequences far beyond a single connection,” said Pradeep Vincent, executive vice president of Compute and AI Infrastructure at OCI, in Oracle’s announcement. Vincent said the company is building Acceleron to respond by rerouting traffic, recovering from failures, and helping customers scale securely.

Front-End RDMA and NVMe Storage

The first new capability, Oracle Acceleron High-Performance Fabric, brings RDMA connectivity to OCI front-end compute, and Oracle said it helps customers deploy inference, storage, and HPC workloads with greater performance. The second, expanded NVMe block-storage attachment options across Oracle Acceleron bare metal and virtual machine shapes, improves performance up to 3x for AI, database, and other data-intensive workloads, according to the announcement.

In a technical post on Oracle’s OCI blog, the authors described RDMA as moving data directly between memory on different machines with minimal CPU involvement, bypassing much of the operating-system and software processing in a conventional transfer to reduce latency, sustain high throughput, and leave more CPU capacity for applications. Those benefits extend beyond GPU-to-GPU communication to storage access and cross-cluster transfers, the authors wrote, but RDMA is highly sensitive to congestion, latency, and packet loss across a shared, mixed-workload network.

One fairness problem is that RDMA typically uses rate-based congestion control while TCP uses window-based control, and depending on traffic patterns one loop can overwhelm the other. To preserve fairness, the post states that Oracle Acceleron SmartNICs use hierarchical token buckets to track bandwidth consumption for RDMA and non-RDMA traffic: a parent bucket represents shared bandwidth with child buckets for each traffic class, and as tokens deplete the SmartNIC probabilistically marks ECN-capable packets or drops packets that cannot respond, producing a gradual congestion signal. The mechanism is work-conserving, so either traffic class can borrow unused bandwidth, and it also addresses incast where a top-of-rack switch connects to a SmartNIC.

For congestion inside the fabric, OCI uses one-way-delay congestion control in the Acceleron SmartNICs. Measuring forward-path delay avoids the false congestion signal a round-trip measurement can produce when only the reverse path is congested, the authors explained. When a flow shows elevated delay, the SmartNIC probes three alternate paths and moves the flow if it finds a better one; otherwise it signals congestion through ECN marking or packet drops. OCI’s front-end RDMA supports all queue pair types and verbs, allowing customers to run existing applications without changes.

Collective Communications and Multi-Planar Failover

The announcement’s distributed-communication capabilities are the Oracle Collective Communications Library, or OCCL, and Transparent Front-End Multiplanar Networking. Oracle said OCCL helps keep distributed AI workloads running when network paths fail by spreading collective traffic across OCI’s backend Multiplanar Network, steering traffic toward healthier paths and recovering when links, ports, or NICs fail. Transparent Front-End Multiplanar Networking brings multiplanar networking to front-end networks with built-in transparent path selection and failover, plus transparent congestion control for front-end RDMA traffic.

The blog frames the problem in training terms: synchronized jobs move only as fast as the slowest link in the chain, so a single congested or failed path can produce a workload-wide slowdown, and standard equal-cost multi-path forwarding pins flows to paths without knowing their sizes, producing uneven link utilization. Multi-planar fabrics have also required multi-port NICs with specialized functionality, which the authors wrote is not always feasible; OCI built OCCL to remove that constraint for general AI and machine learning workloads.

OCCL is a plugin for commonly adopted collective-communication libraries, which AI frameworks use to coordinate data exchange among GPUs. According to the post, it distributes traffic across multiple queue pairs, sends more traffic over queue pairs experiencing higher throughput, and actively moves traffic to less congested paths, implementing congestion-aware message-level and chunk-level load balancing through the standard CCL network-plugin interface. It dynamically assigns collective-communication messages across standard Reliable Connection queue pairs based on observed congestion and health, and it supports port-level and NIC-level failover, steering collective communication after a link or NIC failure so distributed training continues without a job restart.

Moving failover, recovery, and congestion-aware load balancing into the CCL layer decouples those functions from network hardware and firmware and works on both single-planar and multi-planar networks, the authors wrote, adding that customers do not need complex routing controllers to select paths or detect path changes. The post, dated October 27, 2026, was written by Jag Brar, vice president and distinguished engineer for OCI Networking Ops; Vincent; and Joy Ganguly, an OCI architect.