{"id":61,"date":"2026-06-14T16:55:57","date_gmt":"2026-06-14T16:55:57","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/rdp-ai-supercluster-starter-pod-edge\/"},"modified":"2026-07-06T01:47:55","modified_gmt":"2026-07-06T01:47:55","slug":"draco-512x-b200-sxm-ai-supercluster","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/draco-512x-b200-sxm-ai-supercluster\/","title":{"rendered":"DRACO 512\u00d7 B200 SXM AI SuperCluster"},"content":{"rendered":"<p>The DRACO 512\u00d7 B200 SXM AI SuperCluster is a turnkey, liquid-cooled multi-rack AI supercluster delivering ~92 TB HBM3e of aggregate GPU memory across 64 HGX B200 nodes. It arrives as a complete, validated system \u2014 power, cooling, fabric, storage and software \u2014 ready to train frontier models on-premises, in INR, on a GST invoice.<\/p>\n<p>Engineered for sovereign-AI programmes, neoclouds and large enterprises building data-hall-scale capacity, it is delivered as a single engagement: RDP designs the reference architecture, integrates and burns it in, and hands over one validated supercluster with one warranty and one support contract \u2014 removing multi-vendor integration risk.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>512\u00d7 B200 SXM \u00b7 ~92 TB HBM3e aggregate<\/strong> \u2014 data-hall-scale GPU memory for training and serving the largest models.<\/li>\n<li><strong>NVLink\/NVSwitch per node + non-blocking InfiniBand spine<\/strong> \u2014 NVLink + NVSwitch within each HGX node, joined by a non-blocking spine-leaf InfiniBand fabric across the 64 nodes.<\/li>\n<li><strong>128\u00d7 Intel Xeon 6 (64 nodes) + 128 TB DDR5 ECC<\/strong> \u2014 host\/CPU compute matched to 512 Blackwell GPUs.<\/li>\n<li><strong>8 PB parallel NVMe parallel filesystem<\/strong> \u2014 high-throughput training data and checkpoint storage at cluster scale.<\/li>\n<li><strong>8-Rack pod, liquid-cooled, turnkey<\/strong> \u2014 delivered racked, cabled, cooled, validated; one SKU, one warranty.<\/li>\n<li><strong>On-prem data sovereignty<\/strong> \u2014 training data and weights stay in-house; DPDP-friendly, air-gappable.<\/li>\n<li><strong>Make-in-India OEM<\/strong> \u2014 predictable INR pricing, GST tax invoice (HSN 8471), pan-India onsite support, GeM-procurable.<\/li>\n<li><strong>Scale path<\/strong> \u2014 extend the fabric to larger SuperPODs and multi-pod data halls.<\/li>\n<\/ul>\n<h3>AI workload fit (what it actually runs \u2014 honestly)<\/h3>\n<ul>\n<li><strong>Frontier training:<\/strong> distributed pre-training of large foundation models across the 512 GPUs with 3D parallelism.<\/li>\n<li><strong>Large-scale fine-tuning &amp; serving:<\/strong> fine-tune and serve many large models in parallel, or shard the very largest across nodes.<\/li>\n<li><strong>RAG, multimodal &amp; agentic platforms:<\/strong> organisation-wide production AI on the cluster&#8217;s storage and fabric.<\/li>\n<li><em>Engineering note:<\/em> at supercluster scale the network is the limiter, not a single GPU \u2014 a non-blocking spine-leaf InfiniBand fabric keeps collective (all-reduce) traffic scaling across 512 GPUs. Real efficiency depends on model and parallelism strategy; we run a scaling test on your workload.<\/li>\n<\/ul>\n<h3>AI workload positioning<\/h3>\n<p>This sits at the <strong>data-hall \/ foundation-model<\/strong> stage: a complete AI supercomputer. With ~92 TB HBM3e of GPU memory on a non-blocking fabric, it is sized to <strong>sustain<\/strong> frontier-model training and organisation-wide serving on-prem \u2014 the sovereign, owned alternative to renting a hyperscale cloud cluster.<\/p>\n<h3>Industry use cases<\/h3>\n<ul>\n<li><strong>Government &amp; sovereign AI<\/strong> \u2014 a national foundation-model supercluster on GeM-procurable infrastructure.<\/li>\n<li><strong>Neocloud \/ AI providers<\/strong> \u2014 a competitive GPU-cloud region built and validated end-to-end.<\/li>\n<li><strong>BFSI &amp; conglomerates<\/strong> \u2014 private large-model training under data-residency rules.<\/li>\n<li><strong>Healthcare &amp; life sciences<\/strong> \u2014 large-scale research and imaging programmes, data in-house.<\/li>\n<li><strong>Research &amp; national labs<\/strong> \u2014 an institutional AI supercomputer.<\/li>\n<li><strong>Telecom<\/strong> \u2014 in-house foundation-model development at scale.<\/li>\n<\/ul>\n<h3>Performance \u2014 and how to be sure<\/h3>\n<p>We don&#8217;t publish inflated peak numbers. The honest picture: 512 Blackwell GPUs (~92 TB HBM3e) on a non-blocking fabric are sized for frontier-scale distributed training and organisation-wide serving. <strong>Want certainty? Request a free scaling benchmark of your model and dataset on a representative configuration before you commit<\/strong>; we&#8217;ll send back real tokens\/sec, scaling efficiency and projected time-to-train.<\/p>\n<h3>Series &amp; upgrade path<\/h3>\n<ul>\n<li><strong>DRACO<\/strong> (flagship supercluster tier) \u2014 <em>this<\/em>.<\/li>\n<li><strong>Scale ladder:<\/strong> SXM pods (256\u2013512 GPU) and GB200\/GB300 NVL72 SuperPODs (hundreds\u2013thousands of GPUs).<\/li>\n<li><strong>When to step up:<\/strong> extend to larger GB300 SuperPODs or multi-pod data halls \u2014 talk to an architect about the fabric and facility.<\/li>\n<\/ul>\n<h3>On-prem vs cloud \u2014 the TCO case<\/h3>\n<p>At supercluster scale, owning is a strategic and economic decision: an always-on hyperscale cloud cluster dominates any AI budget over a multi-year horizon, while on-prem removes egress and keeps sovereign data and weights in-house. RDP pricing is fixed in INR with a GST input-credit-eligible invoice \u2014 ask for a <strong>multi-year TCO and financing model<\/strong>.<\/p>\n<h3>Software &amp; day-one readiness<\/h3>\n<p>Ships <strong>pre-integrated and validated<\/strong>: NVIDIA driver, CUDA, cuDNN, NCCL, the InfiniBand stack, Slurm and\/or Kubernetes, container registry, PyTorch and vLLM \/ Triton \/ TensorRT-LLM on Ubuntu LTS, with monitoring and a scheduler. Optional managed cluster operations and an MLOps platform.<\/p>\n<h3>Power, cooling &amp; rack integration<\/h3>\n<p>A 8-rack pod liquid-cooled supercluster with high power density \u2014 RDP scopes facility power, CDU\/manifold and water, the InfiniBand spine, floor layout and redundancy in the reference-architecture design. <em>(Exact power, BTU, flow and facility figures confirmed in the design package.)<\/em> Full out-of-band management.<\/p>\n<h3>Deployment, warranty &amp; support<\/h3>\n<ul>\n<li><strong>Made to order<\/strong>, integrated, racked, cabled, cooled and burned-in in India; project timeline confirmed at quote.<\/li>\n<li><strong>Delivered as one system:<\/strong> nodes, leaf\/spine switches, cabling, PDUs, cooling, parallel storage, and the full cluster software stack.<\/li>\n<li><strong>Onsite warranty + AMC<\/strong> with pan-India coverage, cluster-level SLAs and an RMA\/escalation path <em>(exact terms confirmed at quote)<\/em>.<\/li>\n<\/ul>\n<h3>Why RDP<\/h3>\n<p>14 years of Make-in-India infrastructure and <strong>300,000+ devices shipped<\/strong>. Indian OEM, INR pricing, GST tax invoice (HSN 8471), pan-India onsite engineers, GeM availability, and DPDP \/ sovereign-AI-ready deployment.<\/p>\n<h3>Buy with confidence<\/h3>\n<p>This is a turnkey AI supercluster, made to order \u2014 <strong>talk to an RDP solution architect<\/strong>, co-design the reference architecture, fabric and data hall, get a multi-year TCO and financing plan, and <strong>run a scaling benchmark before you commit.<\/strong> Request a quote to begin.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>64\u00d7 HGX nodes \u00b7 128\u00d7 Intel Xeon 6 (64 nodes) \u00b7 128 TB DDR5 ECC \u00b7 8 PB parallel NVMe \u00b7 8-Rack pod \u00b7 liquid-cooled<\/p>\n","protected":false},"featured_media":1927,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"DRACO 512\u00d7 B200 SXM AI SuperCluster \u2014 ~92 TB HBM3e, non-blocking InfiniBand | RDP GPU Mart","rank_math_description":"Turnkey on-prem AI supercluster \u2014 512\u00d7 B200 SXM (~92 TB HBM3e), non-blocking InfiniBand, liquid-cooled. Train frontier models on-prem. Make-in-India, GST invoice. Request a quote.","_hermes_jsonld":""},"product_brand":[],"product_cat":[16],"product_tag":[],"class_list":["post-61","product","type-product","status-publish","has-post-thumbnail","product_cat-ai-superclusters","pa_form-factor-rack","pa_gpu-model-nvidia-b200-sxm","pa_industry-automotive-mobility","pa_industry-defence-aerospace","pa_industry-healthcare-life-sciences","pa_industry-media-gaming-entertainment","pa_industry-neocloud","pa_industry-public-sector-sovereign-ai","pa_industry-research-higher-education","pa_series-draco","pa_use-case-agentic-ai","pa_use-case-computer-vision","pa_use-case-generative-ai","pa_use-case-hpc-ai","pa_use-case-inference","pa_use-case-llm-training","pa_use-case-fine-tuning","pa_use-case-nlp-speech","pa_use-case-rag","pa_use-case-sovereign-ai","pa_workload-fit-large-blackwell-ai-supercluster","first","instock","taxable","shipping-taxable","product-type-external"],"_links":{"self":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product\/61","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=61"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media\/1927"}],"wp:attachment":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media?parent=61"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_brand?post=61"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_cat?post=61"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_tag?post=61"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}