InfiniBand vs Ethernet: When Technical Excellence Meets Market Reality

InfiniBand won the high-performance computing war, and it's not even close. While Ethernet dominates the enterprise datacenter with its commodity economics and "good enough" performance, InfiniBand owns every workload where performance actually matters: supercomputers, AI training clusters, high-frequency trading, and high-performance storage. The technology delivers sub-microsecond latency, native RDMA (Remote Direct Memory Access) that bypasses the CPU entirely, and lossless fabric behavior that makes it the only serious choice for tightly coupled parallel computing. Training GPT-4? InfiniBand. Running weather simulations on 100,000 cores? InfiniBand. Building the world's fastest supercomputers? Over 60% use InfiniBand. Ethernet has spent a decade trying to compete with RoCE (RDMA over Converged Ethernet), but RoCE remains a compromise that falls apart at scale, plagued by Priority Flow Control complexity, congestion management nightmares, and latency variability that kills performance for collective operations. InfiniBand's dominance isn't an accident, it's the result of architectural decisions that prioritized performance and determinism from day one. But that doesn't mean InfiniBand is perfect. The 16-bit LID (Local Identifier) address space now limits fabrics to 49,152 endpoints, a real constraint as AI superclusters scale toward 100,000+ GPUs. This isn't an existential crisis, it's an engineering challenge that needs solving as InfiniBand continues scaling to meet AI's insatiable demand for interconnect bandwidth.

Let's explore what makes InfiniBand the right choice for AI and HPC, why Ethernet's attempts to compete keep failing, the real scaling challenges that need addressing, and how InfiniBand is evolving to meet the demands of exascale computing and foundation model training.

What InfiniBand Actually Is

InfiniBand isn't just "fast Ethernet." It's a fundamentally different network architecture designed from the ground up for high-performance computing.

The Core Principles

RDMA as Native Functionality: InfiniBand was designed around Remote Direct Memory Access. Applications can read or write remote memory directly, bypassing the operating system, TCP/IP stack, and CPU entirely. The network adapter (HCA, Host Channel Adapter) handles everything in hardware.

Credit-Based Flow Control: Unlike Ethernet's collision detection or pause frames, InfiniBand uses credit-based flow control. A sender won't transmit data until the receiver confirms it has buffer space available. This creates a lossless fabric where packets are never dropped due to congestion.

Hardware-Managed Connections: InfiniBand uses "Queue Pairs" (QPs) for communication. Applications create QPs, post send/receive requests to them, and the HCA handles everything else. The HCA manages connection state, retransmissions, and ordering in hardware.

Switched Fabric: InfiniBand networks are always switched, never shared medium. Every link is point-to-point, full-duplex, with guaranteed bandwidth.

Low Latency: Sub-microsecond latency is standard. InfiniBand HDR (200 Gbps) delivers around 0.6 microseconds latency. Compare to Ethernet's typical 2-5 microseconds (even with RDMA).

The Speed Evolution

InfiniBand progressed through several generations:

SDR (Single Data Rate): 2.5 Gbps (2001) DDR (Double Data Rate): 5 Gbps (2005) QDR (Quad Data Rate): 10 Gbps (2007) FDR (Fourteen Data Rate): 14 Gbps (2011) EDR (Enhanced Data Rate): 25 Gbps (2014) HDR (High Data Rate): 50 Gbps per lane, 200 Gbps with 4x links (2017) NDR (Next Data Rate): 100 Gbps per lane, 400 Gbps with 4x links (2020) XDR (eXtreme Data Rate): 250 Gbps per lane, 1 Tbps with 4x links (2024)

Each generation maintained backward compatibility in the switch fabric, allowing incremental upgrades.

Why InfiniBand Dominates HPC and AI

High-performance computing and AI training have fundamentally different requirements than enterprise networking, and InfiniBand was purpose-built for exactly these workloads:

MPI Communication Patterns: Message Passing Interface (MPI) is how parallel applications communicate. MPI applications do millions of small messages between processes, and each message needs to complete with minimal latency. InfiniBand's RDMA and sub-microsecond latency are perfect for this. Ethernet's TCP stack adds 2-5 microseconds of latency per message, and at scale this difference is catastrophic for performance. You cannot compete with InfiniBand using TCP.

All-to-All Communication: HPC and AI training applications do collective operations (all-reduce, all-gather) where every node talks to every other node simultaneously. This creates intense network congestion that would cause Ethernet to collapse. InfiniBand's credit-based flow control prevents congestion at the source. Every packet that gets transmitted has a guaranteed buffer at the destination. This makes InfiniBand's performance deterministic under load, while Ethernet degrades unpredictably.

GPU Collective Operations: Training large language models requires constant all-reduce operations across thousands of GPUs. These collectives are the performance bottleneck. InfiniBand's RDMA with hardware-managed operations, combined with GPU-aware communication (GPUDirect), enables efficient collectives. The latency and determinism differences between InfiniBand and Ethernet RoCE translate directly to training time. Days or weeks of difference for large model training.

Deterministic Performance: Scientists running simulations and engineers training models need predictable performance. Variability kills reproducibility. InfiniBand provides this determinism because credit-based flow control and hardware-managed QPs eliminate most sources of jitter. Ethernet has inherent variability due to congestion, retransmissions, and TCP's adaptive behavior.

Zero CPU Overhead: RDMA means the CPU and GPU focus entirely on computation, not networking. The network adapter handles everything in hardware. Ethernet requires CPU cycles for the TCP/IP stack (even optimized implementations). When you're paying $30,000 per GPU, wasting GPU cycles on networking instead of training is inexcusable.

The numbers speak for themselves: as of 2024, InfiniBand connects over 60% of the Top500 supercomputers. For AI training clusters at hyperscale (Meta's RSC, Tesla's Dojo, xAI's Colossus), InfiniBand is the default choice. It's not a "niche" technology, it's the dominant interconnect for workloads that matter.

Ethernet's Response: RoCE

Ethernet couldn't let InfiniBand own the HPC market. The response was RoCE (RDMA over Converged Ethernet), standardized around 2010.

How RoCE Works

RoCE implements the InfiniBand verbs API and RDMA semantics over Ethernet. There are two versions:

RoCEv1: InfiniBand transport directly over Ethernet frames (Layer 2 only). No IP routing, which limits it to a single broadcast domain. Rarely used today.

RoCEv2: InfiniBand transport over UDP/IP. Routable across Layer 3 networks. This is what people mean when they say "RoCE" today.

RoCE provides:

• RDMA functionality (zero-copy, kernel bypass)
• Similar verbs API to InfiniBand (applications can support both)
• Runs on commodity Ethernet switches (with caveats)
• Lower cost (supposedly)

Why RoCE Seems Appealing

The pitch is compelling: get InfiniBand-like performance using your existing Ethernet infrastructure. No need for specialized InfiniBand switches, no separate network, unified fabric for storage and compute.

For enterprise datacenters running mixed workloads (VMs, storage, some HPC), RoCE makes sense. You can run NVMe-oF (NVMe over Fabrics) with RDMA for storage while still supporting regular TCP/IP traffic on the same network.

Why RoCE Fails at Scale

RoCE sounds good in theory. In practice, at scale, it's fundamentally compromised:

Lossless Ethernet is a Hack: RoCE requires Priority Flow Control (PFC) to create artificial losslessness on top of Ethernet's inherently lossy design (RDMA doesn't handle packet loss well). PFC causes head-of-line blocking where one congested flow pauses an entire priority class, affecting completely unrelated flows. This creates unpredictable latency spikes that destroy performance for latency-sensitive workloads. InfiniBand's credit-based flow control is designed from the ground up to be lossless without these pathologies.

Congestion Management is Impossible: Even with PFC, congestion happens. ECN (Explicit Congestion Notification) and DCQCN (datacenter QCN) try to manage it through reactive mechanisms. Configuration requires tuning dozens of parameters across every NIC and switch. Get any parameter slightly wrong and the network either underutilizes bandwidth or experiences congestion storms. InfiniBand's proactive credit-based flow control eliminates these problems entirely.

Switch Buffer Requirements Kill Cost Advantage: Lossless Ethernet needs deep buffers to handle PFC pause frames without dropping packets. Cheap commodity switches have shallow buffers (measured in single-digit megabytes). You need expensive switches with tens of megabytes of buffer memory, completely negating RoCE's supposed cost advantage over InfiniBand. And even with deep buffers, PFC pause storms still happen.

Microbursts Amplify Under PFC: Ethernet's bursty traffic pattern causes momentary congestion. PFC amplifies this into cascading pause propagation. One microburst can pause multiple upstream hops, creating deadlocks or multi-second stalls. InfiniBand's credit system prevents bursts from forming in the first place.

Configuration Complexity is Untenable: Running production RoCE requires:

• PFC configuration on every switch and NIC (one mistake = performance collapse)
• ECN marking thresholds tuned per-topology
• DCQCN parameters tuned for your specific traffic mix
• QoS classes configured consistently everywhere
• Active monitoring to catch PFC storms and deadlocks
• Expertise to troubleshoot intermittent issues that look like application bugs

Most organizations deploying RoCE discover they need a team of networking specialists to keep it running. InfiniBand just works.

The Performance Gap is Real: Even perfectly tuned RoCE has 1.5-3 microsecond latency. InfiniBand delivers under 0.6 microseconds. For applications doing millions of small messages, this 2-3x latency difference directly translates to 2-3x longer execution time. You can't tune your way out of architectural limitations.

Tail Latency Destroys Training Performance: Average latency might look OK, but RoCE's tail latency (99th percentile, 99.9th percentile) is terrible due to PFC stalls and congestion events. For synchronous operations like all-reduce, the slowest GPU determines iteration time. RoCE's tail latency variability means your GPU utilization plummets as GPUs sit idle waiting for stragglers.

Where RoCE Actually Works (Hint: Not at Scale)

RoCE succeeds in limited scenarios:

• Small clusters (100s of servers where tuning is manageable)
• Storage-only networks (lower message rate than compute)
• Organizations with deep Ethernet expertise and tolerance for complexity
• Workloads that can tolerate latency variation

RoCE fails for:

• Large-scale HPC and AI training (1,000+ nodes)
• Applications with synchronous collectives
• Latency-sensitive workloads
• Organizations that want reliability over complexity

This is why Meta, Microsoft, and other hyperscalers building 10,000+ GPU AI training clusters choose InfiniBand despite it costing more. At scale, InfiniBand's architectural advantages overwhelm any cost difference. RoCE's complexity and performance issues make it cheaper to just buy InfiniBand and have it work correctly.

Storage: Where InfiniBand Shines

InfiniBand found a second life in storage networking, particularly with NVMe over Fabrics.

NVMe-oF: InfiniBand vs RoCE

NVMe over Fabrics allows accessing NVMe SSDs over the network with latencies approaching direct-attach. Two transports dominate:

NVMe-oF over InfiniBand: Uses native InfiniBand RDMA. Latency typically 5-10 microseconds for 4KB random reads. Performance scales linearly with more drives and more network bandwidth. The fabric's lossless nature and RDMA efficiency mean you can nearly saturate the SSDs over the network.

NVMe-oF over RoCE: Uses RoCE RDMA over Ethernet. Latency typically 10-20 microseconds. Performance is good but requires careful network tuning. PFC issues can cause latency spikes that hurt storage performance.

NVMe-oF over TCP: Uses regular TCP/IP. Latency 20-50+ microseconds. Works on any Ethernet network without special configuration. Performance is adequate for many use cases but significantly slower than RDMA transports.

For high-performance storage (all-flash arrays, disaggregated storage, database clusters), InfiniBand provides the most consistent low-latency access. RoCE works but requires more care. TCP is the fallback when RDMA isn't available.

Scale-Out Storage

Distributed storage systems (Ceph, Lustre, GPFS) benefit from InfiniBand:

Ceph with InfiniBand: Ceph uses lots of small I/Os. InfiniBand's low latency improves both throughput and consistency. RoCE works but tail latencies can be problematic.

Lustre: The parallel filesystem used in many HPC centers runs beautifully over InfiniBand. The combination of low latency and high bandwidth enables thousands of compute nodes to access shared storage efficiently.

GPFS (Spectrum Scale): IBM's parallel filesystem, common in HPC and AI infrastructure, heavily utilizes RDMA. InfiniBand is preferred, RoCE is supported but with caveats.

The pattern is clear: when storage performance matters most, InfiniBand is the safe choice. RoCE is the compromise when you need unified networking.

The LID Scaling Challenge: Growing Pains of Success

InfiniBand's success in AI has created an interesting problem: the architecture is scaling faster than the original designers anticipated. The 16-bit Local Identifier (LID) address space, which seemed infinite for HPC workloads, is now a constraint that needs addressing as AI training clusters approach unprecedented scales.

Understanding LID

The LID is a 16-bit address assigned to each port in an InfiniBand subnet. It's how switches and endpoints route packets within a subnet, similar to MAC addresses in Ethernet.

16 bits provides 65,536 possible addresses. After reserved addresses, subnet management overhead, and multicast groups, the usable space is approximately 49,152 LIDs per subnet.

This was more than sufficient for traditional HPC. Clusters of 1,000-10,000 nodes were large. Even 20,000-node systems fit comfortably within a single subnet.

AI Changes the Scale Equation

AI training clusters are different. Foundation model training requires massive GPU counts that dwarf traditional HPC:

Current generation: 10,000-25,000 GPUs for large model training Next generation: Estimates suggest 50,000-100,000+ GPUs for future foundation models Each GPU: Typically dual-port HCA consuming two LIDs Plus infrastructure: Switches (each port needs a LID), storage nodes, management systems

A 50,000 GPU cluster with dual-port HCAs needs 100,000+ LIDs. The math doesn't work in a single subnet.

The Multi-Subnet Solution

The InfiniBand approach is multiple subnets connected by routers or gateways. This is a proven architecture pattern that works, with some trade-offs:

Within-subnet performance: Remains InfiniBand's full performance, sub-microsecond latency, native RDMA.

Cross-subnet communication: Adds routing hop but still maintains RDMA semantics. Performance impact is measurable but not catastrophic for most workloads.

Topology design: Intelligent subnet placement (GPUs that communicate frequently in the same subnet) minimizes cross-subnet traffic.

Management: Modern subnet managers handle multi-subnet coordination more gracefully than early implementations.

Real-World Deployments Prove Viability

Major AI clusters are already running multi-subnet InfiniBand successfully. These aren't theoretical limitations, they're engineering challenges that have been solved:

• Clusters with 20,000-30,000 GPUs use multi-subnet designs
• Training pipeline design accounts for subnet boundaries
• Performance remains dramatically better than RoCE alternatives
• Operational complexity is manageable with proper tooling

The Path Forward

Nvidia and the InfiniBand community are addressing scaling in multiple ways:

Extended LID proposals: Work is underway on extended addressing schemes that maintain backward compatibility where possible.

Improved subnet management: Software advances make multi-subnet clusters behave more like single fabrics from the application perspective.

Hierarchical designs: Network topology co-design with training frameworks to optimize communication patterns for multi-subnet reality.

Hybrid approaches: Using InfiniBand within training pods and high-bandwidth Ethernet for pod-to-pod communication where RDMA's advantages matter less.

The LID limitation is a real constraint, but it's not stopping InfiniBand deployments at scale. It's a problem that needs architectural attention as the technology scales to 100,000+ endpoints, and the InfiniBand community is actively working on solutions. Meanwhile, deployed clusters are achieving their performance targets with multi-subnet designs today.

Compare this to RoCE's fundamental architectural issues (PFC, congestion management, tail latency) that can't be solved with better software or routing. LID scaling is an addressing issue, solvable with specification evolution. RoCE's problems are baked into Ethernet's design.

Mellanox to Nvidia: From Independent Innovator to GPU Ecosystem

InfiniBand's modern history is inseparable from Mellanox and its acquisition by Nvidia.

The Mellanox Era

Mellanox Technologies was founded in 1999 as InfiniBand was being standardized. The company became synonymous with InfiniBand, dominating the HCA and switch market.

Mellanox's success came from:

• Technical excellence (consistently fastest, lowest latency HCAs)
• Vertical integration (HCAs, switches, cables, software stack)
• Supporting both InfiniBand and Ethernet (offering RoCE early)
• Strong relationships with HPC centers and researchers

By the 2010s, Mellanox had 80%+ market share in InfiniBand. The only real competition was Intel (who eventually exited the market) and QLogic (marginal player).

Mellanox also pushed Ethernet forward with 25/50/100G NICs and switches, often ahead of competitors. They understood high-performance networking better than anyone.

The Nvidia Acquisition: Vertical Integration for AI

In 2019, Nvidia announced plans to acquire Mellanox for $6.9 billion. The acquisition closed in 2020 after regulatory approval, and it has proven to be strategically brilliant for AI infrastructure.

Why Nvidia's acquisition makes sense:

1. GPU-to-GPU communication is the bottleneck: Training large AI models is limited by inter-GPU communication, not compute. InfiniBand's low latency and high bandwidth directly accelerates training. Nvidia controlling both GPUs and networking enables optimization impossible with separate vendors.
2. Vertical integration enables innovation: NVLink (Nvidia's GPU-to-GPU interconnect) can now extend seamlessly over InfiniBand. GPUDirect RDMA improvements happen faster with both technologies under one roof. The integration between GPU memory and network fabric is tighter than ever possible with separate companies.
3. Complete solution selling: Nvidia can now sell turnkey AI infrastructure: GPUs, networking, DPUs (BlueField SmartNICs), and software stack. Customers get validated, optimized configurations. This reduces deployment time and risk.
4. Accelerated development: Mellanox's development pace has increased under Nvidia. NDR (400G) deployed quickly, XDR (1.6T) is coming, BlueField DPUs evolved rapidly. Nvidia's R&D investment benefits InfiniBand.
5. AI-specific optimizations: Collective operations for AI training receive focused engineering. Features like SHARP (Scalable Hierarchical Aggregation and Reduction Protocol) offload collective operations to the network, dramatically improving all-reduce performance.

Post-acquisition improvements:

• SuperPods: Pre-validated, tested GPU+InfiniBand configurations that just work. Deployment time drops from months to weeks.
• Tighter GPUDirect integration: Lower latency between GPU memory and network, critical for training performance.
• Network-accelerated collectives: SHARP v2 and v3 push all-reduce operations into the network switches, freeing GPUs for computation.
• Unified management: Single pane of glass for GPUs, networking, and DPUs simplifies operations.

The competitive dynamics:

Some worry about single-vendor dependency. But consider the alternatives:

• RoCE from Broadcom/Marvell: Worse performance, complex configuration, tail latency issues
• AWS EFA: Cloud-only, not portable to on-premises
• Build your own fabric: Only viable for hyperscalers with massive engineering teams

For most organizations building AI infrastructure, Nvidia's vertical integration is a feature, not a bug. You get hardware and software that's co-designed and validated together. The risk isn't vendor lock-in, it's choosing inferior technology that costs you weeks of training time.

Innovation continues: Despite "single vendor" concerns, InfiniBand development is accelerating. XDR (1.6T), BlueField-3 DPUs with 400 Gbps throughput, improved SHARP algorithms, better multi-subnet management. The roadmap is aggressive because AI's demands are insatiable.

The acquisition unified the AI infrastructure stack under engineering teams that understand both compute and networking. The result is technology that works better together than separate vendors' products ever could.

The Ethernet Counter-Movement

Ethernet is fighting back, especially in AI infrastructure:

Ultra Ethernet Consortium

Formed in 2023, the Ultra Ethernet Consortium (Intel, AMD, Microsoft, Meta, and others, but notably not Nvidia) aims to create Ethernet specifications optimized for AI/ML workloads.

Goals include:

• Better RDMA over Ethernet (learning from RoCE's problems)
• AI-specific congestion control
• Larger scale support
• Lower latency

This is a direct response to Nvidia's InfiniBand dominance. The pitch: open standards, multi-vendor, leveraging Ethernet's ecosystem.

AWS EFA (Elastic Fabric Adapter)

AWS built their own RDMA-capable networking for EC2. EFA is Ethernet-based but with custom extensions. It provides low-latency, high-bandwidth interconnect for AI training on AWS without InfiniBand.

This shows that hyperscalers with sufficient engineering resources can build alternatives to InfiniBand. But it's AWS-only, not portable.

Broadcom and Marvell

Both companies offer high-performance Ethernet switches and NICs with RDMA support (RoCE). They're pushing Ethernet as the InfiniBand alternative, though they don't match InfiniBand's latency or determinism.

The Future: InfiniBand's Continued Evolution

Where does high-performance networking go from here? The evidence strongly suggests InfiniBand's dominance will continue and expand.

Scenario 1: InfiniBand Remains the AI/HPC Standard (Most Likely)

InfiniBand continues dominating tightly coupled workloads because its technical advantages are fundamental, not incremental. As AI models scale to 1 trillion+ parameters trained on 100,000+ GPUs, the performance gap between InfiniBand and alternatives widens rather than narrows.

Why this is likely:

• The latency advantage (sub-microsecond) can't be matched by Ethernet's architecture
• Credit-based flow control's determinism becomes more critical at scale, not less
• LID scaling challenges are solvable engineering problems, not fundamental limitations
• Nvidia's R&D investment ensures rapid evolution (XDR, beyond)
• No credible competition emerged despite a decade of trying

Timeline: Indefinitely. InfiniBand's architectural advantages don't erode with time.

Scenario 2: Ethernet Gets Good Enough (Unlikely)

Ultra Ethernet Consortium succeeds in bringing InfiniBand-like performance to Ethernet through revolutionary changes. Latency gap closes, lossless operation becomes reliable, configuration simplifies.

Why this is unlikely:

• Ethernet's fundamental architecture (lossy, CSMA/CD heritage) fights against what RDMA needs
• PFC and congestion management issues are baked into Ethernet's design
• "Good enough" doesn't win when training time differences are measured in weeks
• Multi-vendor Ethernet hasn't matched single-vendor InfiniBand performance in 15+ years of trying
• Ultra Ethernet is trying to solve with specification what InfiniBand solved with architecture

Timeline: If ever, 10+ years out, and still unlikely to match InfiniBand's latency/determinism.

Scenario 3: Custom Fabrics for Cloud (Happening but Limited)

Hyperscalers (AWS, Google, Azure) build proprietary interconnects for their specific use cases. AWS EFA, Google's internal fabrics, custom designs optimized for their infrastructure.

Why this is happening:

• Cloud providers have engineering resources to build custom solutions
• Optimization for specific workloads (their ML frameworks, their topologies)
• Reduces dependence on external vendors

Why it's limited:

• Custom fabrics are cloud-only, not portable
• Development cost only makes sense at hyperscaler scale
• Most enterprises can't build custom interconnects
• Performance often doesn't match InfiniBand (AWS EFA is essentially RoCE with extensions)

Timeline: Already happening but remains niche. On-premises customers and smaller cloud users still choose InfiniBand.

Scenario 4: InfiniBand Gets Better Faster (Likely)

InfiniBand continues rapid evolution, staying ahead of alternatives:

XDR and beyond: 1.6 Tbps per port today, 3.2 Tbps coming, with roadmap to 6.4 Tbps. Bandwidth growth continues.

Extended addressing: LID limitation addressed through specification evolution, backward compatibility where possible, bridges where not.

Improved multi-subnet: Software makes multi-subnet fabrics transparent to applications, eliminating the last operational friction.

AI-specific features: SHARP evolution, GPU-aware collectives in hardware, co-design with AI frameworks for optimal performance.

Cost reduction: Manufacturing scale and technology improvement reduce per-port cost, narrowing gap with Ethernet.

This isn't hypothetical. Nvidia's InfiniBand roadmap extends years into the future with clear bandwidth and feature progression. The commitment is real, backed by billions in R&D.

Why InfiniBand Will Keep Winning

The core reason InfiniBand dominates AI and HPC isn't vendor lock-in or historical accident. It's superior architecture:

Latency: You can't architect around microsecond differences at scale. When training does millions of collectives, every microsecond multiplies into hours or days.

Determinism: Credit-based flow control's predictable behavior beats reactive congestion management. This advantage grows with scale.

RDMA maturity: InfiniBand's hardware-managed QPs and RDMA implementation is more mature, more reliable, and lower latency than Ethernet alternatives.

Ecosystem: MPI implementations, AI frameworks, storage systems all deeply optimized for InfiniBand. Switching to alternatives means giving up years of optimization work.

The future of high-performance AI infrastructure is InfiniBand addressing its scaling challenges (solvable) while maintaining its performance advantages (fundamental). Ethernet will continue trying to catch up, as it has for 15 years, and continue falling short at the scales that matter most.

Living with the Choice

Choosing between InfiniBand and Ethernet (RoCE) comes down to priorities:

Choose InfiniBand if:

• Absolute lowest latency matters
• Workload is tightly coupled (MPI, all-reduce heavy AI training)
• Scale is under 50,000 endpoints (or you can manage multiple subnets)
• You're building an HPC cluster or large AI training system
• Budget allows premium pricing
• Vendor lock-in with Nvidia is acceptable

Choose RoCE if:

• You need unified fabric (storage + compute + management)
• Scale is moderate (1,000s not 10,000s)
• You want multi-vendor options
• Staff has Ethernet expertise
• Budget constrains pure InfiniBand deployment
• Slightly higher latency is acceptable
• You're willing to invest in proper PFC/ECN tuning

Choose regular Ethernet (TCP/IP) if:

• Latency requirements are relaxed (milliseconds OK)
• Standard enterprise workloads
• Simplicity matters more than peak performance
• You want maximum compatibility and vendor choice

The reality is that many large datacenters run multiple networks: InfiniBand for GPU interconnect and high-performance storage, RoCE or regular Ethernet for everything else. The unified fabric dream hasn't fully materialized because different workloads have fundamentally different requirements.

The Uncomfortable Truth

InfiniBand is technically superior for its target workloads. The latency advantage is real, the deterministic behavior matters, the RDMA implementation is more mature. But it's a single-vendor technology with scaling limitations and premium pricing.

Ethernet has the ecosystem, the vendor diversity, and the economics. But making Ethernet behave like InfiniBand (lossless, low-latency, RDMA-capable) requires so much complexity and tuning that you might as well have bought InfiniBand in the first place.

There's no perfect answer. InfiniBand dominates because it wins on technical merit where performance matters most, despite all its limitations. Ethernet keeps trying to catch up because the economic and ecosystem advantages are too compelling to ignore.

The future probably involves InfiniBand continuing in its high-performance niche while Ethernet serves everything else, with the line between them slowly shifting as Ethernet improves and InfiniBand hits scaling walls. Neither is going away soon.

Unless someone invents something better, in which case we'll have this same debate all over again with different acronyms. Welcome to networking, where technical excellence and market reality rarely align, and the best solution on paper loses to the good-enough solution with better economics and ecosystem.