{"id":12700,"date":"2026-08-18T22:20:30","date_gmt":"2026-08-18T22:20:30","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/nvidia-dgx-spark-2-node-linked-cluster\/"},"modified":"2026-08-18T23:24:42","modified_gmt":"2026-08-18T23:24:42","slug":"nvidia-dgx-spark-2-node-linked-cluster","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/nvidia-dgx-spark-2-node-linked-cluster\/","title":{"rendered":"NVIDIA DGX Spark 2-Node Linked Cluster"},"content":{"rendered":"

Two Sparks, linked \u2014 and the model class roughly doubles.<\/strong> NVIDIA supports connecting a pair of DGX Spark units over their built-in ConnectX-7 200GbE<\/strong> interfaces, combining 256 GB of unified memory<\/strong> to reach approximately the 405B-parameter<\/strong> class. Still desk-side, still on a normal wall socket, still entirely inside your building.<\/p>\n

Key highlights<\/h3>\n
    \n
  • 2\u00d7 NVIDIA GB10 Grace Blackwell Superchips<\/strong> \u2014 ~2 petaFLOPS FP4 combined.<\/li>\n
  • 256 GB unified LPDDR5x<\/strong> across the pair \u2014 CPU and GPU share the pool on each node.<\/li>\n
  • ~405B-parameter class<\/strong> \u2014 a size that normally demands rented cluster time.<\/li>\n
  • ConnectX-7 200GbE<\/strong> interconnect between units \u2014 the supported NVIDIA link path.<\/li>\n
  • 8 TB NVMe<\/strong> combined for datasets, checkpoints and model weights.<\/li>\n
  • Two 150 mm boxes on a desk \u2014 no rack, no facility cooling, no dedicated circuit<\/strong>.<\/li>\n
  • DGX OS<\/strong> and the full NVIDIA AI stack on both nodes, ready on power-up.<\/li>\n<\/ul>\n

    AI workload fit<\/h3>\n
      \n
    • Local inference<\/strong> at the ~405B class.<\/li>\n
    • Fine-tuning<\/strong> substantially larger models than a single Spark allows.<\/li>\n
    • RAG<\/strong> over private corpora that must never leave the premises.<\/li>\n
    • Agentic AI<\/strong> development against frontier-scale open models.<\/li>\n
    • Generative AI<\/strong>, computer vision<\/strong>, NLP & speech<\/strong> prototyping.<\/li>\n
    • Teaching and demonstrating distributed inference<\/strong> on real hardware.<\/li>\n<\/ul>\n

      AI workload positioning<\/h3>\n

      The cheapest honest route to frontier-scale open weights on-premises. Sweep-level arithmetic makes the point: the largest trending open models have outgrown a single node’s memory, and this pair is the smallest, quietest way to hold that class of model under your own roof. It is a capacity<\/strong> play, not a throughput one \u2014 for production concurrency, step to a GPU server.<\/p>\n

      Industry use cases<\/h3>\n
        \n
      • Research & education:<\/strong> frontier-model experimentation without cluster allocation.<\/li>\n
      • BFSI:<\/strong> evaluate large open models against confidential data, on-premises.<\/li>\n
      • Healthcare:<\/strong> large clinical models on protected patient data under DPDP.<\/li>\n
      • Enterprise & GCCs:<\/strong> an AI platform team’s reference rig for model selection.<\/li>\n
      • Public sector & sovereign:<\/strong> sovereign model evaluation on department premises.<\/li>\n<\/ul>\n

        Performance & how to be sure<\/h3>\n

        Linking two Sparks raises the ceiling on which model fits<\/strong>, not how fast it serves; interconnect over 200GbE is far below on-package bandwidth, so this is a development and evaluation platform rather than a production serving node. Rather than quote a tokens\/sec figure that will not match your workload, RDP offers a “benchmark your model”<\/strong> session: bring the model, precision and context length you actually intend to run, and we will validate it on this exact configuration before you commit.<\/p>\n

        Series & upgrade path<\/h3>\n

        CARINA<\/strong> entry tier. Below: a single DGX Spark<\/strong> (~200B class). Above: the QUASAR and DRACO aiDAPTIV+ workstations<\/strong> for local fine-tuning throughput, then RDP GPU servers (4\u00d7\/8\u00d7 H200, B200, B300) for production concurrency, then rack-scale. Same CUDA stack at every rung.<\/p>\n

        On-prem vs cloud (TCO)<\/h3>\n

        For sustained daily AI work this configuration removes per-GPU-hour billing, queueing for scarce instances, and egress charges on your own data — and keeps everything on-premises for DPDP and data-residency obligations. Cloud still wins for burst capacity and one-off very large training runs; on-prem wins on sustained utilisation, control and predictable INR capital cost. We model the crossover with your actual usage rather than assert it.<\/p>\n

        Software & day-one readiness<\/h3>\n

        Each unit ships with NVIDIA DGX OS<\/strong> and the full NVIDIA AI stack \u2014 CUDA, cuDNN, containers, and the common serving and fine-tuning frameworks (vLLM, PyTorch, NGC catalogue). It is the same CUDA environment as every larger NVIDIA system RDP supplies, which is why work developed here moves upward to a GPU server or rack-scale system without a rewrite.<\/p>\n

        Power, thermal & acoustics<\/h3>\n

        Two compact desktop units, each on a standard 230V socket with an external adapter. Air-cooled, office-appropriate acoustics, no rack or facility work of any kind. Exact wattage, BTU and dB(A) figures come from RDP bench measurement rather than estimates — ask for the site-readiness sheet with your quote.<\/em><\/p>\n

        Deployment, warranty & support<\/h3>\n

        Built to order by RDP Technologies. Supplied with GST invoice (HSN 8471), pan-India onsite support, and availability through GeM for public-sector procurement. Built to order \u2014 typically 4\u20136 weeks, subject to allocation.<\/p>\n

        Why RDP<\/h3>\n

        RDP Technologies is a Make-in-India OEM with 14+ years and 300,000+ units shipped, supplying AI infrastructure from desk-side systems to rack-scale AI factories — with predictable INR pricing, GST invoicing and pan-India onsite service.<\/p>\n

        Buy with confidence<\/h3>\n

        Use Request a Quote<\/strong> to reach an RDP solution architect for sizing, a benchmark-your-model session, financing options and a delivery plan. No obligation.<\/p>\n","protected":false},"excerpt":{"rendered":"

        2\u00d7 GB10 Grace Blackwell \u00b7 256GB unified memory \u00b7 ConnectX-7 200GbE link \u00b7 desktop<\/p>\n","protected":false},"featured_media":12478,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"NVIDIA DGX Spark 2-Node Cluster \u2014 256GB Unified, ~405B Models | RDP GPU Mart","rank_math_description":"Two NVIDIA DGX Spark units linked over ConnectX-7 200GbE: 256GB unified memory, ~405B-parameter class on your desk. On-prem, DPDP-aligned. Request a quote from RDP GPU Mart.","_hermes_jsonld":""},"product_brand":[],"product_cat":[22],"product_tag":[],"class_list":["post-12700","product","type-product","status-publish","has-post-thumbnail","product_cat-agentic-ai-pcs-edge-ai","pa_form-factor-desktop","pa_gpu-model-nvidia-gb10-grace-blackwell","pa_industry-bfsi-hft","pa_industry-enterprise-gccs","pa_industry-healthcare-life-sciences","pa_industry-public-sector-sovereign-ai","pa_industry-research-higher-education","pa_series-carina","pa_use-case-agentic-ai","pa_use-case-computer-vision","pa_use-case-fine-tuning","pa_use-case-generative-ai","pa_use-case-inference","pa_use-case-nlp-speech","pa_use-case-rag","pa_workload-fit-linked-desktop-405b-class-inference","first","instock","taxable","shipping-taxable","product-type-external"],"_links":{"self":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product\/12700","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/types\/product"}],"replies":[{"embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/comments?post=12700"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media\/12478"}],"wp:attachment":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media?parent=12700"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_brand?post=12700"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_cat?post=12700"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_tag?post=12700"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}