Why NVMe-oF Matters: Low Latency, Scalability, and Efficiency
Latency has always been the Achilles’ heel of storage networking. With spinning disks, a few milliseconds of delay didn’t matter much because the physical media itself was slow. But once flash and SSDs entered the picture, the bottleneck shifted from the device to the protocol stack and the network. Even with locally attached NVMe SSDs, applications can complete I/O in tens of microseconds. Contrast that with traditional SAN protocols like iSCSI or FCP, where each I/O might incur hundreds of microseconds of software and network overhead. That gap is precisely what NVMe-oF addresses.
Technically, NVMe-oF extends the NVMe command set across a network fabric with minimal translation. It avoids the SCSI command emulation layer, which is where much of the overhead in iSCSI or Fibre Channel comes from. Instead, NVMe-oF supports direct submission and completion queues across fabrics, allowing I/O requests to flow directly between application and SSD with very little intervention. The result is latency in the range of 20–30 microseconds over a fabric, which is close to the performance of local NVMe drives.
Scalability is equally important. NVMe was built from the ground up to support massive parallelism, with thousands of submission and completion queues. NVMe-oF preserves this across the network. Instead of a single bottlenecked command queue like in legacy protocols, applications and hosts can open dedicated queues mapped directly to CPU cores. This design allows an infrastructure to handle millions of IOPS per host without the inefficiency of context switching or queue locking. For modern multi-core servers running dozens of containers or VMs, this is essential to maintaining predictable performance at scale.
Efficiency closes the loop. In traditional stacks, high IOPS means high CPU burn; the protocol overhead eats into compute cycles that should be reserved for applications. NVMe-oF dramatically reduces this penalty. Benchmarks often show that NVMe-oF can deliver up to 3–4x the IOPS per CPU core compared to iSCSI, enabling data centers to consolidate infrastructure without sacrificing performance. This is why hyperscalers and cloud providers see NVMe-oF not just as a performance play, but as a TCO optimization.
From a use case perspective, this matters in environments where every microsecond counts:
- Databases that require sub-millisecond response times at high transaction rates.
- AI/ML training pipelines, where GPUs are idle if storage can’t keep up.
- Edge workloads, where latency-sensitive applications (autonomous systems, 5G, IoT) can’t tolerate long storage paths.
- Real-time analytics, where streams of incoming data must be processed without bottlenecks.
In all these scenarios, NVMe-oF ensures storage isn’t the limiting factor. It allows enterprises to design infrastructure where the network behaves almost like direct-attached flash, but with the flexibility and scalability of shared storage.
Choosing the Right Fabric: RDMA, Fibre Channel, or TCP?
NVMe-oF isn’t a single protocol but a framework: it defines how NVMe commands can be transported across a variety of network fabrics. Each transport has its strengths, limitations, and best-fit scenarios. Understanding these trade-offs is critical for architects who want to maximize performance without overcomplicating operations.
When NVMe commands traverse a fabric, they don’t move raw across the wire. Instead, they are wrapped into lightweight containers called capsules. A capsule may carry just the command itself or, in some cases, the command and its associated data. This encapsulation is what allows NVMe’s queue-based model to be extended cleanly across different transports like Fibre Channel, RDMA, or TCP. It adds very little overhead while preserving the efficiency of NVMe’s direct submission and completion queues, which is why NVMe-oF can deliver latencies close to those of locally attached drives.
RDMA (RoCE and iWARP)
RDMA (Remote Direct Memory Access) is the gold standard for low latency in NVMe-oF. By design, RDMA bypasses the host CPU and kernel for data transfers, moving data directly from the memory of one host to another. This means an NVMe command can be issued and completed with minimal CPU involvement, often resulting in latencies as low as 10–20 microseconds across the fabric.
- RoCE (RDMA over Converged Ethernet) is the most widely used variant, but it requires a lossless Ethernet fabric (achieved with Data Center Bridging or PFC). This can complicate network design and troubleshooting.
- iWARP, in contrast, runs over TCP and doesn’t need a lossless fabric. However, it has limited ecosystem adoption, and most vendors prioritize RoCE for their NVMe-oF solutions.
- InfiniBand is another transport that implements RDMA natively. It’s common in high-performance computing environments where ultra-low latency and extremely high throughput are critical.
Best use case: high-performance clusters, AI/ML pipelines, financial services, or any workload where the lowest possible latency is non-negotiable.
Trade-offs:
- Requires specialized NICs with RDMA support.
- Can be complex to configure and troubleshoot (especially with RoCE).
- Limited interoperability across different vendors in multi-vendor environments.
Fibre Channel (FC-NVMe)
Fibre Channel is a trusted workhorse in enterprise storage. With FC-NVMe, organizations can run NVMe commands over existing FC fabrics without ripping and replacing infrastructure. For enterprises heavily invested in SANs, this is the most natural way to adopt NVMe-oF.
FC’s advantages are its maturity, stability, and tooling. Storage admins who’ve managed FC environments for years can adopt FC-NVMe with minimal retraining. Performance is strong, with latencies typically in the 50–100 microsecond range – not as low as RDMA, but still a major leap from legacy SCSI over FC.
Best use case: enterprises with existing FC SAN deployments looking to modernize without overhauling their networks.
Trade-offs:
- Requires FC HBAs and FC switches (cannot leverage existing Ethernet networks).
- Vendor ecosystems are narrower compared to Ethernet-based approaches.
- Operational silos: networking teams may lack FC expertise, which remains a specialized skill set.
TCP (NVMe/TCP)
The newest entrant, NVMe/TCP, takes a pragmatic approach: it allows NVMe commands to be transported over standard TCP/IP networks. No specialized NICs, no lossless Ethernet requirements. If you have an IP network, you can deploy NVMe/TCP.
While TCP introduces more overhead than RDMA, modern CPUs and NIC offload features have narrowed the performance gap significantly. Latency for NVMe/TCP typically falls in the 100–200 microsecond range; higher than RDMA but still much lower than iSCSI or legacy protocols. For most enterprise workloads, this is “fast enough,” and the simplicity of deployment often outweighs the modest latency trade-off.
Best use case: organizations that want NVMe-oF benefits without investing in specialized hardware or re-architecting their networks. Ideal for cloud environments, brownfield data centers, and Kubernetes-native platforms.
Trade-offs:
- Slightly higher latency compared to RDMA and FC.
- Relies on CPU for transport, which can impact performance under very heavy loads (though DPU and NIC offloads are evolving to address this).
- Ecosystem is still maturing compared to RDMA and FC.
Putting It Together
The fabric decision isn’t about “which is best overall” but “which is best for my workload and environment.”
- If ultra-low latency is critical and you have the skills to manage a lossless Ethernet fabric, choose RDMA (RoCE).
- If you already have a stable FC SAN, FC-NVMe is the lowest-friction path.
- If simplicity and broad adoption are more important than squeezing out the last microsecond, NVMe/TCP is the future-proof choice.
In practice, many organizations will adopt a hybrid approach: RDMA for their high-performance clusters, TCP for container-native storage in Kubernetes, and FC-NVMe to extend the life of their SAN investments.
NVMe-oF in Modern Architectures
The real power of NVMe-over-Fabrics emerges not just in benchmarks, but in how it reshapes the design of modern infrastructure. By extending the low-latency characteristics of NVMe across the network, NVMe-oF removes one of the last big bottlenecks in data-centric computing: shared storage performance. This shift is influencing several architectural models at once – from tightly integrated clusters to massively parallel supercomputing systems. Below, we explore four key areas where NVMe-oF is becoming foundational:
Hyperconverged Infrastructure (HCI)
Hyperconverged infrastructure designs merge compute, storage, and networking into a single system. The challenge has always been that once storage is shared across nodes, performance consistency suffers. Traditional stacks introduce bottlenecks through protocol overhead and inefficient I/O paths.
With NVMe-oF, nodes in a cluster can expose their local NVMe drives to peers with almost no additional latency. Submission and completion queues can be mapped across the fabric, so remote access feels nearly identical to local access. In practice, this turns a collection of drives scattered across servers into a unified, high-performance storage pool.
This has two major benefits: workloads with strict latency requirements can run directly on HCI without requiring a separate SAN, and performance scales linearly as nodes are added. For mixed environments running databases, analytics engines, and virtual desktops, this eliminates one of the biggest trade-offs of hyperconvergence.
Software-Defined Storage
Software-defined storage (SDS) platforms aggregate storage across multiple nodes into a logical pool, abstracted and managed by software. The Achilles’ heel has always been the network: no matter how fast the drives, the inter-node communication determines overall performance.
NVMe-oF helps SDS systems achieve near-local performance characteristics. By cutting fabric overhead, a read or write request traveling across nodes incurs tens of microseconds of latency rather than hundreds. This allows SDS to support latency-sensitive workloads that were previously relegated to dedicated arrays.
The protocol’s parallelism also supports multi-tenant or multi-application environments. Thousands of submission and completion queues can be assigned per tenant or workload, reducing contention and noisy-neighbor effects. In practice, this means predictable performance even when dozens of independent clients share the same distributed storage pool.
Parallel File Systems
In high-performance computing and large-scale data analytics, parallel file systems allow thousands of clients to access the same dataset concurrently. These systems are often bottlenecked not by raw media speed but by the latency and throughput of the fabric connecting compute and storage.
NVMe-oF addresses this by enabling direct, low-latency access from compute nodes to NVMe-backed storage targets. Instead of I/O requests traversing multiple translation layers, commands are issued natively across the fabric. With RDMA transports, latencies can drop into the tens of microseconds even when scaled to thousands of nodes. With TCP transports, organizations can deploy parallel file systems over commodity Ethernet while still achieving leaps in performance compared to legacy NFS or iSCSI.
The result is more efficient use of compute clusters. CPUs and GPUs spend less time waiting on data and more time processing it. For scientific simulations, training large-scale AI models, or analyzing petabyte-scale datasets, these improvements directly shorten time-to-results.
Container-Native Storage
Containers are inherently ephemeral, but the applications they run often are not. Stateful workloads such as databases, messaging systems, and AI pipelines need persistent storage that can match the agility of the container model.
NVMe-oF enables container-native storage platforms to expose persistent volumes with the same low-latency profile as local NVMe drives, while maintaining the flexibility of shared infrastructure. Pods can attach and detach block volumes dynamically, with response times measured in microseconds instead of milliseconds.
Because support for NVMe-oF is already integrated into modern operating systems, container storage drivers can implement it without additional layers of emulation. This reduces complexity while ensuring that high-performance workloads (for example, stateful databases inside Kubernetes clusters) no longer require a compromise between agility and speed.
Conclusion
The real story of NVMe-over-Fabrics isn’t about command sets or microseconds shaved off the I/O path. It is about how infrastructure evolves when storage is no longer the limiting factor. Once storage can scale in parallel with compute and network, new design patterns emerge — architectures that are more fluid, efficient, and aligned with the way applications actually demand data.
What makes NVMe-oF powerful is that it fades into the background. Applications don’t need to know whether their data is local or remote; developers don’t have to compromise between agility and performance; architects don’t have to choose between efficiency and scale. When NVMe-oF is in place, the storage fabric simply keeps up.
Looking ahead, the role of NVMe-oF will likely deepen as new accelerators, smart network devices, and memory-semantic fabrics enter the data center. But its purpose will remain the same: removing distance as a constraint, so data can move as quickly and seamlessly as modern workloads demand. For organizations, the question isn’t whether NVMe-oF is faster. It is whether they are ready to design systems that fully take advantage of a world where storage performance is no longer the bottleneck.
Contact DataCore to learn how NVMe-oF applies to our data storage offerings, and how it can accelerate the performance, scalability, and efficiency of your infrastructure.
