{"id":13,"date":"2026-06-14T16:32:41","date_gmt":"2026-06-14T16:32:41","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/rdp-ai-supercluster-fleet-multi-rack\/"},"modified":"2026-07-06T01:48:09","modified_gmt":"2026-07-06T01:48:09","slug":"draco-32x-gb300-nvl72-ai-supercluster","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/draco-32x-gb300-nvl72-ai-supercluster\/","title":{"rendered":"DRACO 32\u00d7 GB300 NVL72 AI SuperCluster"},"content":{"rendered":"<p>The DRACO 32\u00d7 GB300 NVL72 AI SuperCluster is a turnkey, liquid-cooled GB300 NVL72 SuperPOD \u2014 2304 Grace-Blackwell Ultra GPUs across 32 unified NVLink-domain racks, joined by a non-blocking spine-leaf InfiniBand fabric, delivering ~664 TB HBM3e of aggregate GPU memory. It arrives as a complete, validated AI factory \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 national-scale enterprises, it is delivered as a single engagement: RDP designs the reference architecture, integrates and burns it in, and hands over one validated SuperPOD with one warranty and one support contract \u2014 removing multi-vendor integration risk at frontier scale.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>32\u00d7 GB300 NVL72 \u00b7 ~664 TB HBM3e aggregate<\/strong> \u2014 2304 Grace-Blackwell Ultra GPUs for the largest foundation-model training.<\/li>\n<li><strong>Unified NVLink domains + non-blocking InfiniBand spine<\/strong> \u2014 each NVL72 rack is one 72-GPU accelerator; the racks scale over a full-bisection fabric.<\/li>\n<li><strong>1,152\u00d7 NVIDIA Grace (ARM) (coherent) + Grace LPDDR5X coherent memory<\/strong> \u2014 Grace CPUs coherently attached to the Blackwell Ultra GPUs.<\/li>\n<li><strong>32 PB parallel NVMe parallel filesystem<\/strong> \u2014 high-throughput training data and checkpoint storage at SuperPOD scale.<\/li>\n<li><strong>Multi-Rack data hall, 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 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 the largest foundation models across 2304 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 NVLink domains.<\/li>\n<li><strong>RAG, multimodal &amp; agentic platforms:<\/strong> national- or organisation-wide production AI on the SuperPOD&#8217;s storage and fabric.<\/li>\n<li><em>Engineering note:<\/em> a GB300 SuperPOD combines unified NVLink domains (72 Blackwell Ultra GPUs each, 288 GB per GPU) over a non-blocking InfiniBand spine \u2014 within a rack the GPUs act as one accelerator, across racks the spine carries collectives. This is the architecture frontier-model training actually uses; we validate real scaling for your workload, not a peak number.<\/li>\n<\/ul>\n<h3>AI workload positioning<\/h3>\n<p>This sits at the top of the <strong>data-hall \/ foundation-model<\/strong> stage: a complete frontier AI supercomputer. With ~664 TB HBM3e of GPU memory on a non-blocking fabric, it is sized to <strong>sustain<\/strong> the largest training runs and national-scale serving on-prem \u2014 the sovereign, owned alternative to a hyperscale cloud region.<\/p>\n<h3>Industry use cases<\/h3>\n<ul>\n<li><strong>Government &amp; sovereign AI<\/strong> \u2014 a national foundation-model SuperPOD on GeM-procurable infrastructure.<\/li>\n<li><strong>Neocloud \/ AI providers<\/strong> \u2014 a premium GPU-cloud region built and validated end-to-end.<\/li>\n<li><strong>BFSI &amp; conglomerates<\/strong> \u2014 private frontier-model training under data-residency rules.<\/li>\n<li><strong>Healthcare &amp; life sciences<\/strong> \u2014 national-scale research and imaging programmes, data in-house.<\/li>\n<li><strong>Research &amp; national labs<\/strong> \u2014 a frontier AI supercomputer.<\/li>\n<li><strong>Telecom &amp; public sector<\/strong> \u2014 sovereign foundation-model platforms.<\/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: 2304 Grace-Blackwell Ultra GPUs (~664 TB HBM3e) on a non-blocking fabric are sized for the largest foundation-model training and national-scale 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> 8-rack \u2192 16-rack \u2192 multi-rack data hall GB300 SuperPODs.<\/li>\n<li><strong>When to step up:<\/strong> extend to a multi-pod data hall \u2014 talk to an architect about the fabric, power and facility.<\/li>\n<\/ul>\n<h3>On-prem vs cloud \u2014 the TCO case<\/h3>\n<p>At SuperPOD scale, owning is a sovereign and economic decision: an always-on hyperscale cloud region 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 multi-rack data hall liquid-cooled SuperPOD with very 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> NVL72 racks, 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 frontier AI SuperPOD, 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>32\u00d7 GB300 NVL72 \u00b7 1,152\u00d7 NVIDIA Grace (ARM) \u00b7 Grace LPDDR5X coherent memory \u00b7 32 PB parallel NVMe \u00b7 Multi-Rack data hall \u00b7 liquid-cooled<\/p>\n","protected":false},"featured_media":1923,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"DRACO 32\u00d7 GB300 NVL72 AI SuperCluster \u2014 ~664 TB HBM3e, Grace-Blackwell Ultra | RDP GPU Mart","rank_math_description":"Turnkey on-prem GB300 NVL72 SuperPOD \u2014 32\u00d7 GB300 NVL72 (~664 TB HBM3e), unified NVLink domains + non-blocking InfiniBand, liquid-cooled. Train frontier models on-prem. Make-in-India, GST invoice. 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