Amid the growing global demand for data center capacity, NVIDIA today unveiled its latest innovation — the X800 series network switches — at the company’s Global Developer Conference. This launch marks a significant step forward in NVIDIA’s effort to optimize trillion-parameter GPU computing and artificial intelligence (AI) infrastructure.
The NVIDIA Quantum-X800 InfiniBand switch series represents NVIDIA’s newest generation of networking platforms, purpose-built for high-performance computing (HPC), AI, and hyperscale data centers. Supporting XDR (800 Gb/s) speeds, the series delivers exceptional bandwidth and ultra-low latency. Introduced in 2024, it is designed to power the training and inference of trillion-parameter AI models, making it ideal for AI factories, cloud infrastructures, and scientific simulations. It inherits InfiniBand technology from NVIDIA Mellanox and integrates silicon photonics, enhancing power efficiency, connectivity simplicity, and overall system performance.
As the next-generation platform following Quantum-2, Quantum-X800 further advances port speed, switch density, and in-network computing capabilities. It also provides deeper software integration and optimization with mainstream communication frameworks. A single switch supports 144 ports at 800 Gb/s, enabling FP8 in-network computation. In addition, the SuperNIC includes a built-in PCIe 6.0 switch, allowing direct connections between GPUs and CPUs — removing the traditional dependence on CPU or PCIe switch bandwidth for data exchange. This architecture dramatically enhances performance for AI, data processing, and high-performance computing workloads.
The NVIDIA Quantum-X800 InfiniBand switch focuses on high-performance networking innovations designed to support trillion-parameter AI model training, HPC, and hyperscale data centers. Powered by the NVIDIA Quantum-3 ASIC, it delivers 800 Gb/s (XDR) speeds, ultra-low latency, and intelligent acceleration, significantly enhancing network efficiency and scalability.
Each port supports 800 Gb/s with full bidirectional bandwidth, providing a total throughput of up to 115.2 Tb/s (for example, in the Q3400-RA model with 144 ports). It adopts the OSFP interface, enabling flexible port breakout (e.g., from 800 G to 400 G or lower), and supports two-tier fat-tree topologies for non-blocking large-scale GPU interconnects.
The Quantum-X800 features hardware-accelerated SHARP v4 (Scalable Hierarchical Aggregation and Reduction Protocol), supporting collective operations such as All-Reduce, data aggregation, and MPI_Alltoall. It also includes MPI tag matching, programmable cores, and MPI acceleration.
Compared with the previous generation, the switching bandwidth capacity of the Quantum-X800 has increased fivefold, enabling it to handle significantly higher data traffic without network congestion. Meanwhile, its in-network computing performance has improved by up to nine times, thanks to NVIDIA’s SHARP technology.
The SHARP technology acts as a performance booster for in-network computation, allowing the Quantum-X800 to process complex AI workloads more efficiently and intelligently.
Delivers port-to-port latency under 100 nanoseconds, combined with adaptive routing and congestion control to ensure network stability under heavy loads. It also features self-healing network technology that automatically detects and repairs link failures.
Incorporates Co-Packaged Optics (CPO) technology, integrating optical modules with the ASIC to reduce fiber connections and optimize power efficiency. Supports Remote Direct Memory Access (RDMA) for enhanced data transfer performance. This design lowers power consumption (typically <5 kW for a 4U system), simplifies cabling, and improves overall network density and reliability.
Integrates NVIDIA Unified Fabric Manager (UFM) for network monitoring, automated troubleshooting, and multi-tenant management. It is fully compatible with the HPC-X software stack, including NCCL, SHMEM, and MPI libraries.
The architecture of Quantum-X800 makes it particularly suitable for environments requiring extreme network performance. Here are the primary scenarios:
Large-Scale AI Training and Generative AI: Supports training of trillion-parameter models (e.g., LLMs) by accelerating collective operations in-network via SHARP v4, reducing communication bottlenecks and server load. Ideal for AI factories and multi-site interconnections, boosting training efficiency by up to 9x.
High-Performance Computing (HPC) and Scientific Simulations: Provides maximum throughput for climate modeling, drug discovery, and physics simulations. When combined with NVIDIA Blackwell GPUs, it builds efficient scientific computing infrastructures, supporting expansions beyond 10,000 GPUs.
Hyperscale Data Centers and Cloud Infrastructure: Optimizes cloud-native HPC/AI deployments with multi-tenant isolation and self-healing networks. Suitable for enterprise-level cloud services, offering low TCO and reliable end-to-end performance.
GPU Computing and AI Infrastructure Expansion: Tailored for GPU-intensive workloads, such as real-time inference and distributed computing. Silicon photonics integration simplifies cabling, making it suitable for million-scale GPU AI infrastructures.
Quantum-X800 outperforms Ethernet solutions in these scenarios, especially in latency-sensitive and compute-intensive tasks, but is best suited for dedicated infrastructures.
The NVIDIA Quantum-X800 and Spectrum-X800 represent the pinnacle of InfiniBand and Ethernet interconnect technologies, respectively, in the 800G era. Both are built on NVIDIA’s latest high-speed switching architecture but are optimized for different use cases and performance priorities:
Quantum-X800 focuses on AI training and HPC supercomputing clusters that demand ultra-low latency and in-network computing. It is based on the InfiniBand protocol, delivering high performance and low latency for large-scale AI training and scientific computing.
Spectrum-X800, on the other hand, targets AI cloud data centers and Ethernet-based infrastructures, emphasizing open standards and massive scalability. It is optimized for Ethernet protocols, offering multi-tenant isolation and cloud-native compatibility.
| Dimension | Quantum-X800 (InfiniBand) | Spectrum-X800 (Ethernet) | Analysis |
|---|---|---|---|
| Latency | < 100 ns | ~5–10 µs | InfiniBand is more efficient for AI synchronization. |
| Bandwidth | 115.2 Tb/s | 51.2 Tb/s | InfiniBand supports higher-density deployments. |
| Compute Acceleration | SHARP v4 (9× improvement) | RoCEv2 & BlueField-3 | InfiniBand offloads more computation. |
| Cost | Higher, but lower TCO | More economical and easier to integrate | Ethernet suits mid-scale environments. |
| Application | Hyperscale AI training | Cloud AI services & inference | InfiniBand dominates compute-intensive workloads. |
Overall, the Quantum-X800 outperforms Ethernet in terms of latency and efficiency, especially in clusters with 32 or more nodes, while Ethernet offers easier integration and upgrades within existing infrastructure.
The launch of the NVIDIA Quantum-X800 InfiniBand Switch marks the beginning of the 800G XDR era for InfiniBand networking. With unmatched bandwidth, ultra-low latency, and advanced in-network computing, it stands as a cornerstone for the next generation of AI training, HPC, and cloud infrastructure.
Looking ahead, as generative AI and trillion-parameter models continue to evolve, Quantum-X800 will work in synergy with NVIDIA’s GPUs, DPUs, and software stack to build a more efficient, intelligent, and scalable compute fabric, accelerating the transformation of global AI and data centers.
