{"id":19,"date":"2026-06-14T16:32:41","date_gmt":"2026-06-14T16:32:41","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/rdp-gx8-hgx-8-gpu-server\/"},"modified":"2026-07-06T01:48:07","modified_gmt":"2026-07-06T01:48:07","slug":"draco-8x-h200-sxm-gpu-server","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/draco-8x-h200-sxm-gpu-server\/","title":{"rendered":"DRACO 8\u00d7 H200 SXM GPU Server"},"content":{"rendered":"<p>The DRACO 8\u00d7 H200 SXM GPU Server is a Rack 8U rack server built to bring training and high-throughput inference of the largest models into your own data centre. Eight NVIDIA H200 SXM5 (HGX H200) GPUs on a single HGX baseboard deliver 1,128 GB HBM3e of HBM3e, linked by NVLink and NVSwitch into one tightly-coupled accelerator \u2014 sized to train and serve up to 405B-class models, behind your firewall, in INR, on a GST invoice.<\/p>\n<p>Engineered for AI platform teams bringing large-model training in-house, it pairs the eight GPUs with a 2\u00d7 Intel Xeon 6 host, 2 TB DDR5 ECC and 60 TB NVMe, with 8\u00d7 400G InfiniBand for scale-out, redundant power and full BMC\/IPMI \u2014 a data-centre training node that racks and runs.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>1,128 GB HBM3e of HBM3e across 8\u00d7 H200 SXM<\/strong> \u2014 train and serve up to 405B-class models on a single node.<\/li>\n<li><strong>NVLink + NVSwitch fabric<\/strong> \u2014 full all-to-all GPU bandwidth for efficient tensor-parallel training, the defining advantage of an HGX node.<\/li>\n<li><strong>2\u00d7 Intel Xeon 6 + 2 TB DDR5 ECC<\/strong> \u2014 high core count and memory bandwidth to feed eight data-centre GPUs.<\/li>\n<li><strong>Rack 8U, liquid-cooled, redundant PSU, BMC\/IPMI<\/strong> \u2014 sustained all-GPU clocks, hot-swap drives, lights-out management.<\/li>\n<li><strong>60 TB NVMe + 8\u00d7 InfiniBand NDR 400G<\/strong> \u2014 large local dataset\/checkpoint capacity with a high-bandwidth scale-out fabric; no egress fees.<\/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 multiple HGX nodes scale out over InfiniBand into RDP rack-scale systems and superclusters.<\/li>\n<\/ul>\n<h3>AI workload fit (what it actually runs \u2014 honestly)<\/h3>\n<ul>\n<li><strong>Training &amp; fine-tuning:<\/strong> full pre-training\/fine-tuning and parameter-efficient (QLoRA\/LoRA) training of 405B-class models, tensor- and pipeline-parallel across the eight NVSwitch-linked GPUs.<\/li>\n<li><strong>Inference:<\/strong> serve 405B-class models at high throughput, or host several large models concurrently.<\/li>\n<li><strong>RAG, vision, multimodal &amp; agentic:<\/strong> production pipelines on the 60 TB NVMe and multi-agent back-ends.<\/li>\n<li><em>Engineering note:<\/em> the 8 SXM GPUs sit on an HGX baseboard with <strong>NVLink + NVSwitch<\/strong> \u2014 full all-to-all GPU bandwidth for efficient <strong>tensor-parallel<\/strong> training of the largest models, exactly what frontier-scale training needs.<\/li>\n<\/ul>\n<h3>AI workload positioning<\/h3>\n<p>This sits at the <strong>train-and-serve<\/strong> stage of the AI lifecycle, at data-centre scale. With 1,128 GB HBM3e of HBM3e on an NVLink+NVSwitch fabric, a 2\u00d7 Intel Xeon 6 host and 400G InfiniBand, it is sized to <strong>sustain<\/strong> real large-model training and production serving \u2014 where renting eight H200-class cloud GPUs around the clock becomes the dominant line in an AI budget.<\/p>\n<h3>Industry use cases<\/h3>\n<ul>\n<li><strong>BFSI<\/strong> \u2014 train and serve private large models for risk, fraud and document intelligence under data-residency rules.<\/li>\n<li><strong>Healthcare &amp; life sciences<\/strong> \u2014 on-prem training of medical and imaging models, PHI in-house.<\/li>\n<li><strong>Government &amp; PSU<\/strong> \u2014 sovereign large-model training on GeM-procurable infrastructure.<\/li>\n<li><strong>Neocloud \/ AI providers<\/strong> \u2014 a node to build a GPU cloud or training service.<\/li>\n<li><strong>Telecom &amp; manufacturing<\/strong> \u2014 large-scale model development near operations.<\/li>\n<li><strong>Research &amp; higher-ed<\/strong> \u2014 an institutional large-model training node.<\/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: 1,128 GB HBM3e of HBM3e across eight NVSwitch-linked GPUs is sized to train\/fine-tune and serve up to 405B-class models on-prem. <strong>Want certainty? Request a free benchmark of your model and dataset on this exact configuration before you buy<\/strong> \u2014 we&#8217;ll send back real tokens\/sec and training\/fine-tune timings for your workload.<\/p>\n<h3>Series &amp; upgrade path<\/h3>\n<ul>\n<li><strong>DRACO<\/strong> (flagship training tier) \u2014 <em>this<\/em>.<\/li>\n<li><strong>GPU-count ladder:<\/strong> 4-GPU \u2192 8-GPU within the line; step up to Blackwell B200\/B300 HGX nodes for higher memory and the largest models.<\/li>\n<li><strong>When to step up:<\/strong> for multi-rack scale, move to RDP Rack-Scale AI Systems and AI SuperClusters \u2014 talk to an architect about the fabric.<\/li>\n<\/ul>\n<h3>On-prem vs cloud \u2014 the TCO case<\/h3>\n<p>For sustained training and inference, owning beats renting: eight continuously-running H200-class cloud GPUs add up fast, 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 TCO comparison<\/strong> against your current cloud spend.<\/p>\n<h3>Software &amp; day-one readiness<\/h3>\n<p>Ships <strong>pre-configured to train and serve<\/strong>: NVIDIA driver, CUDA, cuDNN, NCCL, the InfiniBand stack, Docker and NVIDIA Container Toolkit, with PyTorch, vLLM \/ Triton \/ TensorRT-LLM on Ubuntu LTS. Optional Slurm\/Kubernetes, managed AI-stack and observability setup available.<\/p>\n<h3>Power, cooling &amp; rack integration<\/h3>\n<p>An Rack 8U liquid-cooled node with redundant PSUs and substantial power draw \u2014 this node is liquid-cooled; plan CDU\/manifold and facility water, plus InfiniBand cabling. <em>(Exact PSU rating, BTU, flow, fabric cabling and rack-depth figures confirmed on the build sheet.)<\/em> BMC\/IPMI provides remote power, console and health monitoring.<\/p>\n<h3>Deployment, warranty &amp; support<\/h3>\n<ul>\n<li><strong>Made to order<\/strong>, built, racked-and-stacked, cabled and burned-in in India; realistic lead time confirmed at quote.<\/li>\n<li><strong>In the box:<\/strong> server, rails, power cables, quick-start, and the pre-installed AI software stack; fabric switches and cabling scoped at quote.<\/li>\n<li><strong>Onsite warranty + AMC<\/strong> with pan-India coverage 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 data-centre training-and-serving server, made to order \u2014 <strong>talk to an RDP solution architect<\/strong>, get a configuration and 3-year TCO tailored to your workload, and <strong>benchmark your own model on it before you commit.<\/strong> Request a quote to begin.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>2\u00d7 Intel Xeon 6 \u00b7 2 TB DDR5 ECC \u00b7 60 TB NVMe \u00b7 8U rack<\/p>\n","protected":false},"featured_media":2257,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"DRACO 8\u00d7 H200 SXM GPU Server \u2014 2\u00d7 Intel Xeon 6, 2 TB DDR5 ECC, 1,128 GB HBM3e GPU | RDP GPU Mart","rank_math_description":"On-prem H200 SXM HGX server \u2014 8\u00d7 H200 SXM (1,128 GB HBM3e), 2\u00d7 Intel Xeon 6, 2 TB DDR5 ECC, 60 TB NVMe, NVLink+NVSwitch. Train & serve up to 405B on-prem. Make-in-India, GST invoice, pan-India onsite. 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