{"id":16,"date":"2026-06-14T16:32:41","date_gmt":"2026-06-14T16:32:41","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/rdp-h200-8-node-rack\/"},"modified":"2026-07-06T01:48:09","modified_gmt":"2026-07-06T01:48:09","slug":"draco-64x-h200-sxm-rack-scale-ai-system","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/draco-64x-h200-sxm-rack-scale-ai-system\/","title":{"rendered":"DRACO 64\u00d7 H200 SXM Rack-Scale AI System"},"content":{"rendered":"<p>The DRACO 64\u00d7 H200 SXM Rack-Scale AI System is a turnkey, liquid-cooled full-rack AI training cluster \u2014 64 NVIDIA H200 SXM GPUs across 8 HGX nodes, wired into one system with NVLink inside each node and a 400G InfiniBand spine between them. It delivers 9,024 GB HBM3e of aggregate HBM3e and arrives racked, cabled, cooled and tested, ready to train and serve the largest models on-premises \u2014 in INR, on a GST invoice.<\/p>\n<p>Engineered for organisations building serious in-house AI capacity, it removes the integration risk of assembling a cluster yourself: RDP sizes the nodes, fabric, storage, power and cooling as one validated system and delivers it as a single SKU with one warranty and one support contract.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>64\u00d7 H200 SXM \u00b7 9,024 GB HBM3e aggregate HBM3e<\/strong> \u2014 cluster-scale GPU memory for trillion-token training and large-model serving.<\/li>\n<li><strong>NVLink + NVSwitch in each node, 400G InfiniBand spine<\/strong> \u2014 full intra-node bandwidth and low-latency inter-node collectives for near-linear scaling.<\/li>\n<li><strong>16\u00d7 Intel Xeon 6 + 16 TB DDR5 ECC<\/strong> \u2014 host compute and memory matched to 64 data-centre GPUs.<\/li>\n<li><strong>480 TB NVMe NVMe + parallel-FS ready<\/strong> \u2014 high-throughput data and checkpoint storage across the cluster.<\/li>\n<li><strong>Full-Rack, liquid-cooled, turnkey<\/strong> \u2014 delivered racked, cabled, cooled and burned-in; 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 grow to larger rack-scale systems and multi-rack AI SuperClusters on the same fabric.<\/li>\n<\/ul>\n<h3>AI workload fit (what it actually runs \u2014 honestly)<\/h3>\n<ul>\n<li><strong>Distributed training:<\/strong> data-, tensor- and pipeline-parallel training of Trillion-scale-class models across the 64 GPUs.<\/li>\n<li><strong>Large-scale inference:<\/strong> serve many large models, or shard the largest models across nodes for high throughput.<\/li>\n<li><strong>RAG, multimodal &amp; agentic at scale:<\/strong> production AI platforms on the cluster&#8217;s storage and fabric.<\/li>\n<li><em>Engineering note:<\/em> within each HGX node the eight GPUs share an NVLink+NVSwitch fabric; across nodes, a 400G InfiniBand spine carries the collective (all-reduce) traffic. Real-world scaling efficiency depends on model and parallelism strategy \u2014 we validate it for 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>cluster-scale train-and-serve<\/strong> stage: a complete, validated AI system rather than a single server. With 9,024 GB HBM3e of HBM3e and a balanced NVLink\/InfiniBand fabric, it is sized to <strong>sustain<\/strong> real distributed training and large-scale serving \u2014 the owned alternative to renting an equivalent 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 or departmental AI cluster on GeM-procurable infrastructure.<\/li>\n<li><strong>BFSI<\/strong> \u2014 a private training cluster for large risk, fraud and language models.<\/li>\n<li><strong>Healthcare &amp; life sciences<\/strong> \u2014 large-scale model and imaging training, data in-house.<\/li>\n<li><strong>Neocloud \/ AI providers<\/strong> \u2014 a validated rack to build or expand a GPU cloud.<\/li>\n<li><strong>Research &amp; higher-ed<\/strong> \u2014 an institutional AI training cluster.<\/li>\n<li><strong>Large enterprise &amp; telecom<\/strong> \u2014 in-house foundation-model and platform development.<\/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: 9,024 GB HBM3e of HBM3e across 64 NVSwitch-linked GPUs on a 400G InfiniBand spine is sized for Trillion-scale-class distributed training and large-scale serving. <strong>Want certainty? Request a free benchmark of your model and dataset \u2014 including a scaling test across the nodes \u2014 on this exact configuration before you buy<\/strong>; we&#8217;ll send back real tokens\/sec, scaling efficiency and timings.<\/p>\n<h3>Series &amp; upgrade path<\/h3>\n<ul>\n<li><strong>DRACO<\/strong> (flagship rack-scale tier) \u2014 <em>this<\/em>.<\/li>\n<li><strong>Scale ladder:<\/strong> 16-GPU quarter-rack \u2192 32-GPU half-rack \u2192 64-GPU full-rack; step up to Blackwell B200\/GB200\/GB300 rack-scale systems for more memory per GPU.<\/li>\n<li><strong>When to step up:<\/strong> for multi-rack scale, move to RDP AI SuperClusters built from these systems \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>For a sustained training cluster, owning beats renting decisively: an always-on cloud cluster of this size is the dominant line in an AI budget, and on-prem removes egress fees and keeps data and weights in-house. RDP pricing is fixed in INR with a GST input-credit-eligible invoice \u2014 ask for a <strong>3-year cluster TCO comparison<\/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, Docker and NVIDIA Container Toolkit, with Slurm or Kubernetes, PyTorch and vLLM \/ Triton \/ TensorRT-LLM on Ubuntu LTS. Optional managed cluster operations, scheduler and observability setup.<\/p>\n<h3>Power, cooling &amp; rack integration<\/h3>\n<p>A full-rack liquid-cooled system \u2014 plan facility power, CDU\/manifold and water, and the InfiniBand spine. RDP scopes power, cooling and floor\/rack requirements as part of the design. <em>(Exact PSU\/PDU ratings, BTU, flow and facility figures confirmed on the build sheet.)<\/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; realistic lead time confirmed at quote.<\/li>\n<li><strong>Delivered as one system:<\/strong> nodes, fabric switches, cabling, PDUs, cooling integration, and the pre-installed cluster software stack.<\/li>\n<li><strong>Onsite warranty + AMC<\/strong> with pan-India coverage, cluster-level support and an RMA\/escalation path <em>(exact term &amp; response window 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 rack-scale AI system, made to order \u2014 <strong>talk to an RDP solution architect<\/strong>, size the cluster, fabric and facility, get a 3-year TCO, and <strong>benchmark your own model with a scaling test before you commit.<\/strong> Request a quote to begin.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>8-node HGX H200 \u00b7 16\u00d7 Intel Xeon 6 \u00b7 16 TB DDR5 ECC \u00b7 480 TB NVMe \u00b7 Full-Rack \u00b7 liquid-cooled<\/p>\n","protected":false},"featured_media":1931,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"DRACO 64\u00d7 H200 SXM Rack-Scale AI System \u2014 8-node HGX H200, 9,024 GB HBM3e HBM | RDP GPU Mart","rank_math_description":"Turnkey full-rack AI training cluster \u2014 64\u00d7 H200 SXM (9,024 GB HBM3e), 8-node HGX, NVLink + 400G InfiniBand, liquid-cooled. Train Trillion-scale-class models on-prem. Make-in-India, GST invoice. 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