{"id":8820,"date":"2026-07-24T07:38:33","date_gmt":"2026-07-24T07:38:33","guid":{"rendered":"https:\/\/openzeka.com\/en\/?post_type=product&p=8820"},"modified":"2026-07-27T13:44:47","modified_gmt":"2026-07-27T13:44:47","slug":"nvidia-dgx-spark-quad-ai-cluster-4-node-512-gb-200gbe","status":"publish","type":"product","link":"https:\/\/openzeka.com\/en\/product\/nvidia-dgx-spark-quad-ai-cluster-4-node-512-gb-200gbe\/","title":{"rendered":"NVIDIA DGX Spark Quad AI Cluster \u2013 4 Node, 512 GB, 200GbE"},"content":{"rendered":"
<\/span><\/div>

NVIDIA DGX Spark Quad AI Cluster<\/b><\/h3><\/div>

The NVIDIA DGX Spark Quad AI Cluster is a compact and scalable AI cluster that integrates four NVIDIA DGX Spark systems through a high-speed, switch-based network architecture.<\/p>\n

The package includes four NVIDIA DGX Spark systems, one MikroTik CRS812 compute switch, two 400G QSFP-DD to 2 \u00d7 200G QSFP56 breakout cables, one MikroTik CRS312 in-band management switch, and six Cat6 10GbE Ethernet cables.<\/p>\n

The DGX Spark Quad AI Cluster can be delivered either without installation or with complimentary pre-installation by OpenZeka, depending on the customer’s preference. With the uninstalled option, all components are shipped in their standard packaging, and installation is performed by the customer. If complimentary pre-installation is selected, the compute and management networks are configured, all connections are verified, and basic cluster communication is validated prior to shipment.<\/p>\n

Each DGX Spark node connects to the compute switch via a 200GbE link using the NVIDIA ConnectX-7 network interface. The compute network supports RoCEv2-based RDMA communication, enabling NCCL collective operations, model parallelism, and high-speed data transfer for distributed AI workloads. Management, SSH access, internet connectivity, model downloads, and standard network traffic are handled over a separate 10GbE management network. This architecture dedicates the high-speed compute network exclusively to inter-node AI communication.<\/p>\n<\/div>

<\/span><\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div>

Scalable AI Infrastructure with Four Nodes
\n<\/b><\/p>\n

Each NVIDIA DGX Spark system includes 128 GB of coherent unified system memory. The four-node configuration provides a total of:<\/p>\n

    \n
  • 512 GB of distributed unified memory capacity<\/li>\n
  • \u00a016 TB of local NVMe storage<\/li>\n<\/ul>\n

    The four-node architecture provides a suitable development environment for:<\/p>\n

      \n
    • Running large models that cannot fit into the total memory capacity of one, two, or three Spark systems by distributing them across four nodes using tensor parallelism, pipeline parallelism, or other supported distributed methods<\/li>\n
    • Running models that can fit on fewer Spark systems with higher throughput when appropriate parallelization support is available<\/li>\n
    • Providing greater total capacity for model weights, KV cache, and runtime memory in long context length scenarios<\/li>\n
    • Increasing overall system capacity in inference scenarios where more concurrent users or requests are served by the same model<\/li>\n
    • Running large language models through distributed inference methods<\/li>\n
    • Multi-node communication based on NCCL and MPI<\/li>\n
    • RAG and agentic AI applications<\/li>\n
    • High-speed node-to-node data communication over RoCEv2\/RDMA<\/li>\n
    • RDMA-based infrastructure designed to reduce network processing overhead on host CPUs during GPU communication<\/li>\n
    • Multimodal generative AI workloads<\/li>\n
    • Model quantization and performance testing<\/li>\n
    • Prototyping distributed architectures before transitioning to data center environments<\/li>\n<\/ul>\n

      Model compatibility and achievable performance vary depending on the model architecture, quantization format, context length, KV cache requirements, framework support, batch size, and parallelization method.<\/p>\n<\/div>

      <\/span><\/div>

      Switch-Based Compute Network
      \n<\/b><\/p>\n

      The compute network is built on the MikroTik CRS812 switch.<\/p>\n

      The CRS812 provides the following high-speed connectivity ports:<\/p>\n

        \n
      • 2 \u00d7 400G QSFP56-DD ports<\/li>\n
      • 2 \u00d7 200G QSFP56 ports<\/li>\n
      • 8 \u00d7 50G SFP56 ports<\/li>\n<\/ul>\n

        The switch also features RouterOS v7, a quad-core 2 GHz ARM processor, 4 GB of RAM, redundant hot-swappable power supplies, and hot-swappable fans. MikroTik positions this model for AI cluster environments and laboratory deployments that require high-speed east-west traffic.<\/p>\n

        In the DGX Spark Quad configuration, two 400G ports of the CRS812 are used. Each 400G port is split into two 200G QSFP56 connections using a passive breakout cable. This provides a dedicated 200GbE physical connection for each Spark system.<\/p>\n

        The DGX Spark Quad compute network is configured to leverage the RoCEv2 and RDMA capabilities of NVIDIA ConnectX-7 adapters. RoCEv2 enables RDMA communication over Ethernet and IP infrastructure, providing high-bandwidth data transfer between nodes with low communication overhead.<\/p>\n

        This architecture is particularly used for intensive data transfers between nodes during NCCL collective operations, tensor parallelism, pipeline parallelism, and distributed inference workloads.<\/p>\n

        RoCE performance can be affected by switch queues, MTU configuration, traffic classification, PFC\/flow control, ECN, and software-level settings. In OpenZeka deployments, link speeds, RDMA devices, and inter-node communication are additionally verified.<\/p>\n<\/div>

        <\/span><\/div>

        Dedicated 10GbE Management Network
        \n<\/b><\/p>\n

        The in-band management network is provided through the MikroTik CRS312-4C+8XG-RM.<\/p>\n

        The CRS312 features:<\/p>\n

          \n
        • 8 \u00d7 10G RJ45 Ethernet ports<\/li>\n
        • 4 \u00d7 10G RJ45\/SFP+ combo ports<\/li>\n
        • 120 Gbps non-blocking throughput<\/li>\n
        • 240 Gbps switching capacity<\/li>\n
        • RouterOS v7 or SwitchOS support<\/li>\n<\/ul>\n

          Each Spark system is connected to the CRS312 using a single Cat6 Ethernet cable. This network is used for:<\/p>\n

            \n
          • SSH access<\/li>\n
          • System management<\/li>\n
          • Software updates<\/li>\n
          • Model downloads<\/li>\n
          • Internet access<\/li>\n
          • Monitoring and log traffic<\/li>\n
          • Optional NAS access<\/li>\n<\/ul>\n

            The separation of compute and management networks prevents standard management and internet traffic from traversing the ConnectX-7 compute links, ensuring that the high-speed network is dedicated to distributed AI communication.<\/p>\n<\/div>

            <\/span><\/div>

            Optional Shared NAS Storage
            \n<\/b><\/p>\n

            The cluster can optionally be delivered with an ASUSTOR Lockerstor AS6808T NAS and eight NAS-grade drives. In the standard example configuration, 8 \u00d7 8 TB Western Digital WD80EFPX-68C4ZN0<\/strong> drives are used. The disk brand, model number, and capacity can be customized based on stock availability or project requirements.<\/p>\n

            Example 8 \u00d7 8 TB Configuration:<\/p>\n\n\n\n\n\n\n\n
            \n

            Configuration<\/b><\/p>\n<\/td>\n

            		\n

            Approximate Raw Capacity<\/b><\/p>\n<\/td>\n

            		\n

            Disk Protection<\/b><\/p>\n<\/td>\n<\/tr>\n

            \n

            RAW\/JBOD<\/p>\n<\/td>\n

            		\n

            64 TB<\/p>\n<\/td>\n

            		\n

            Depends on configuration<\/p>\n<\/td>\n<\/tr>\n

            \n

            RAID 0<\/p>\n<\/td>\n

            		\n

            64 TB<\/p>\n<\/td>\n

            		\n

            None<\/p>\n<\/td>\n<\/tr>\n

            \n

            RAID 5<\/p>\n<\/td>\n

            		\n

            56 TB<\/p>\n<\/td>\n

            		\n

            1 disk<\/p>\n<\/td>\n<\/tr>\n

            \n

            RAID 6<\/p>\n<\/td>\n

            		\n

            48 TB<\/p>\n<\/td>\n

            		\n

            2 disks<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            The actual usable capacity will be lower than the raw values shown in the table due to the disk manufacturer’s capacity calculation method, RAID metadata, file system overhead, and system-reserved space.<\/p>\n

            The NAS is connected to the MikroTik CRS312 switch using dual LACP-supported links with two Cat6 Ethernet cables.<\/p>\n

            Bonding can provide advantages such as:<\/p>\n

              \n
            • Link redundancy<\/li>\n
            • Load balancing across multiple clients<\/li>\n
            • Distribution of multiple simultaneous data streams<\/li>\n<\/ul>\n

              The disk brand, disk capacity, and RAID configuration can be customized according to project requirements. Depending on stock availability, technically equivalent NAS-grade drives with the same capacity and usage class may be provided instead of the specified disk model.<\/p>\n<\/div>

              <\/span><\/div>

              OpenZeka Cluster Solution
              \n<\/b><\/p>\n

              The DGX Spark Quad AI Cluster is not simply a hardware package consisting of four computers placed together. By integrating a high-speed compute network, a dedicated management network, breakout connectivity infrastructure, and optional shared storage, it provides a comprehensive infrastructure designed for distributed AI workloads.<\/p>\n

              The system can be delivered either as a complete uninstalled package with all components included or shipped with complimentary pre-installation performed by OpenZeka, depending on customer preference. As part of the complimentary pre-installation service, compute and management connections are configured, 200GbE links are verified, basic node access is established, and cluster communication is validated.<\/p>\n

              Additional services such as customer-specific model deployment, custom network policies, enterprise integrations, data migration, application installation, and performance optimization are evaluated separately within the scope of each project.<\/p>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"

              NVIDIA DGX Spark Quad AI Cluster<\/strong><\/p>\n

              This package includes four NVIDIA DGX Spark systems, one MikroTik CRS812 high-speed compute switch, one MikroTik CRS312 10GbE in-band management switch, and the required compute and management connection cables.<\/p>\n

              The DGX Spark Quad AI Cluster combines four Grace Blackwell systems into a switch-based high-speed network architecture. Each node connects to a 200GbE compute network via NVIDIA ConnectX-7, while a separate 10GbE Ethernet network is used for system management and standard network traffic.<\/p>\n

              The compute network supports RoCEv2-based RDMA communication, enabling high-bandwidth data transfer between nodes for NCCL and distributed AI workloads. This architecture isolates AI communication from internet and management traffic.<\/p>\n

              \n
              \n
              In OpenZeka benchmarks, large language models from the GLM-5.2, Qwen3.5, Qwen3.6, and Gemma model families were deployed on this four-node cluster. The evaluated models included GLM-5.2-INT4, Qwen3.5-397B-A17B-INT4, Gemma-4-31B-it-NVFP4, Qwen3.6-27B-NVFP4, and Qwen3.6-35B-A3B-NVFP4.<\/div>\n
              \n
              \n
              <\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"featured_media":8821,"template":"","meta":[],"pwb-brand":[],"product_brand":[],"product_cat":[1689],"product_tag":[1687,1688],"class_list":{"0":"post-8820","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","6":"product_cat-dgx-systems","7":"product_tag-dgx-spark","8":"product_tag-nvidia-dgx-spark","9":"pa_cpu-20-core-arm-10-cortex-x925-10-cortex-a725-arm","11":"first","12":"instock","13":"shipping-taxable","14":"product-type-simple"},"yoast_head":"\nNVIDIA DGX Spark Quad AI Cluster \u2013 4 Node, 512 GB, 200GbE - OpenZeka | NVIDIA Embedded Distributor<\/title>\n<meta name=\"description\" content=\"The NVIDIA DGX Spark Quad AI Cluster is a compact and scalable AI cluster that integrates four NVIDIA DGX Spark systems through a high-speed, switch-based network architecture.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/openzeka.com\/en\/product\/nvidia-dgx-spark-quad-ai-cluster-4-node-512-gb-200gbe\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"NVIDIA DGX Spark Quad AI Cluster \u2013 4 Node, 512 GB, 200GbE\" \/>\n<meta property=\"og:description\" content=\"The NVIDIA DGX Spark Quad AI Cluster is a compact and scalable AI cluster that integrates four NVIDIA DGX Spark systems through a high-speed, switch-based network architecture.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/openzeka.com\/en\/product\/nvidia-dgx-spark-quad-ai-cluster-4-node-512-gb-200gbe\/\" \/>\n<meta property=\"og:site_name\" content=\"OpenZeka | NVIDIA Embedded Distributor\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/openzeka\" \/>\n<meta property=\"article:modified_time\" content=\"2026-07-27T13:44:47+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/openzeka.com\/en\/wp-content\/uploads\/2026\/07\/spark-4-1.2-1.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"1200\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@openzeka\" \/>\n<meta name=\"twitter:label1\" content=\"Price\" \/>\n\t<meta name=\"twitter:data1\" content=\"\" \/>\n\t<meta name=\"twitter:label2\" content=\"Availability\" \/>\n\t<meta name=\"twitter:data2\" content=\"In stock\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/openzeka.com\\\/en\\\/product\\\/nvidia-dgx-spark-quad-ai-cluster-4-node-512-gb-200gbe\\\/\",\"url\":\"https:\\\/\\\/openzeka.com\\\/en\\\/product\\\/nvidia-dgx-spark-quad-ai-cluster-4-node-512-gb-200gbe\\\/\",\"name\":\"NVIDIA DGX Spark Quad AI Cluster \u2013 4 Node, 512 GB, 200GbE - 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