{"id":11,"date":"2026-06-14T16:32:40","date_gmt":"2026-06-14T16:32:40","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/rdp-ai-supercluster-starter-pod\/"},"modified":"2026-07-06T01:48:09","modified_gmt":"2026-07-06T01:48:09","slug":"draco-256x-h200-sxm-ai-supercluster","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/draco-256x-h200-sxm-ai-supercluster\/","title":{"rendered":"DRACO 256\u00d7 H200 SXM AI SuperCluster"},"content":{"rendered":"<p>The DRACO 256\u00d7 H200 SXM AI SuperCluster is a turnkey, liquid-cooled multi-rack AI supercluster \u2014 256 NVIDIA H200 SXM GPUs across 32 HGX nodes in a 4-rack pod, wired with a non-blocking spine-leaf InfiniBand fabric. It delivers ~36 TB HBM3e of aggregate GPU memory and 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 the multi-vendor integration risk of building it yourself.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>256\u00d7 H200 SXM \u00b7 ~36 TB HBM3e aggregate<\/strong> \u2014 data-hall-scale GPU memory for training and serving the largest models.<\/li>\n<li><strong>Non-blocking spine-leaf InfiniBand (NDR\/XDR)<\/strong> \u2014 full-bisection bandwidth for near-linear scaling across all 32 nodes.<\/li>\n<li><strong>NVLink + NVSwitch within each node<\/strong> \u2014 full intra-node bandwidth, complemented by the InfiniBand spine between nodes.<\/li>\n<li><strong>64\u00d7 Intel Xeon 6 (32 nodes) + 64 TB DDR5 ECC<\/strong> \u2014 host compute and memory matched to 256 data-centre GPUs.<\/li>\n<li><strong>4 PB parallel NVMe parallel filesystem<\/strong> \u2014 high-throughput training data and checkpoint storage at cluster scale.<\/li>\n<li><strong>4-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<\/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 256 GPUs with 3D parallelism (data, tensor, pipeline).<\/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> production AI platforms for an entire organisation on the cluster&#8217;s storage and fabric.<\/li>\n<li><em>Engineering note:<\/em> at supercluster scale the limiting factor is the network, not a single GPU \u2014 this system uses a non-blocking spine-leaf InfiniBand fabric so collective (all-reduce) traffic scales. Real efficiency depends on model and parallelism strategy; we run a scaling test on your workload rather than quoting a peak FLOPS number.<\/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 rather than a server or single rack. With ~36 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, where the meter never stops.<\/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 and platform 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: 256 H200 SXM GPUs (~36 TB HBM3e) on a non-blocking InfiniBand 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> 256-GPU pod \u2192 512-GPU pod \u2192 GB200\/GB300 NVL72 SuperPODs (hundreds\u2013thousands of GPUs).<\/li>\n<li><strong>When to step up:<\/strong> for unified NVLink domains at rack scale, see RDP NVL72 Rack-Scale systems; for the largest SuperPODs, scale this fabric out \u2014 talk to an architect about the data hall.<\/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 job scheduler. Optional managed cluster operations and an MLOps platform.<\/p>\n<h3>Power, cooling &amp; rack integration<\/h3>\n<p>A 4-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 as part of the reference-architecture design. <em>(Exact power, BTU, flow and facility figures confirmed in the design package.)<\/em> Full out-of-band management across the cluster.<\/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>32\u00d7 HGX H200 nodes \u00b7 64\u00d7 Intel Xeon 6 (32 nodes) \u00b7 64 TB DDR5 ECC \u00b7 4 PB parallel NVMe \u00b7 4-Rack pod \u00b7 liquid-cooled<\/p>\n","protected":false},"featured_media":2066,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"DRACO 256\u00d7 H200 SXM AI SuperCluster \u2014 32 HGX nodes, ~36 TB HBM3e | RDP GPU Mart","rank_math_description":"Turnkey on-prem AI supercluster \u2014 256\u00d7 H200 SXM (~36 TB HBM3e) across 32 nodes, non-blocking InfiniBand, liquid-cooled. Train frontier models on-prem. Make-in-India, GST invoice. 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