{"id":74,"date":"2026-06-14T16:55:57","date_gmt":"2026-06-14T16:55:57","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/rdp-gx4-4-gpu-server-pro\/"},"modified":"2026-07-06T01:47:53","modified_gmt":"2026-07-06T01:47:53","slug":"draco-4x-h200-sxm-gpu-server","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/draco-4x-h200-sxm-gpu-server\/","title":{"rendered":"DRACO 4\u00d7 H200 SXM GPU Server"},"content":{"rendered":"<p>The DRACO 4\u00d7 H200 SXM GPU Server is a Rack 4U rack server built to bring training and high-throughput inference into your own data centre. 4 NVIDIA H200 SXM5 (HGX H200 baseboard) GPUs deliver 564 GB HBM3e of high-bandwidth GPU memory in a dense, serviceable chassis \u2014 sized to train and serve 180B-class models, behind your firewall, in INR, on a GST invoice.<\/p>\n<p>Engineered for AI platform and MLOps teams standardising training and large-model serving on owned infrastructure, it pairs the GPUs with a 2\u00d7 Intel Xeon 6 host, 1.5 TB DDR5 ECC and 30 TB NVMe, with redundant power and full BMC\/IPMI remote management \u2014 a production node that racks and runs, not a repurposed desktop.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>564 GB HBM3e of GPU memory across 4\u00d7 H200 SXM<\/strong> \u2014 train and serve 180B-class models on-prem.<\/li>\n<li><strong>NVLink + NVSwitch fabric<\/strong> \u2014 full all-to-all GPU bandwidth for tensor-parallel models, ECC throughout.<\/li>\n<li><strong>2\u00d7 Intel Xeon 6 + 1.5 TB DDR5 ECC<\/strong> \u2014 high core count and memory bandwidth to feed 4 data-centre GPUs.<\/li>\n<li><strong>Rack 4U, redundant PSU, BMC\/IPMI<\/strong> \u2014 hot-swap drives, tool-less service, lights-out management.<\/li>\n<li><strong>30 TB NVMe + InfiniBand NDR 400G<\/strong> \u2014 fast dataset, checkpoint and weight storage with high-throughput networking; no egress fees.<\/li>\n<li><strong>On-prem data sovereignty<\/strong> \u2014 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 within the DRACO server line and out to RDP rack-scale systems as demand rises.<\/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 and parameter-efficient (QLoRA\/LoRA) fine-tuning and training of 180B-class models, with tensor- and data-parallelism across the GPUs.<\/li>\n<li><strong>Inference:<\/strong> serve 180B-class models at high throughput, or host several large models concurrently.<\/li>\n<li><strong>RAG, vision, multimodal &amp; agentic:<\/strong> production RAG endpoints, vision\/multimodal inference and multi-agent back-ends on the 30 TB NVMe.<\/li>\n<li><em>Engineering note:<\/em> the 4 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. With 564 GB HBM3e of GPU memory, an NVLink+NVSwitch fabric, a 2\u00d7 Intel Xeon 6 host and fast NVMe, it is sized to <strong>sustain<\/strong> real training runs and production serving \u2014 where renting equivalent 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 private model training and serving for fraud, risk and document intelligence under data-residency rules.<\/li>\n<li><strong>Healthcare &amp; life sciences<\/strong> \u2014 on-prem clinical NLP and imaging model training, PHI in-house.<\/li>\n<li><strong>Government &amp; PSU<\/strong> \u2014 sovereign AI on GeM-procurable infrastructure.<\/li>\n<li><strong>SaaS \/ product<\/strong> \u2014 own your training and serving stack instead of renting.<\/li>\n<li><strong>Telecom &amp; manufacturing<\/strong> \u2014 AI close to operations.<\/li>\n<li><strong>Research &amp; higher-ed<\/strong> \u2014 a shared institutional 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: 564 GB HBM3e of GPU memory across 4 GPUs is sized to train\/fine-tune and serve 180B-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> 2-GPU \u2192 4-GPU \u2192 8-GPU within the line; step up to higher-memory SXM nodes (B200\/B300) for 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: 4 continuously-running cloud GPUs of this class 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>A Rack 4U air-cooled node with redundant PSUs and substantial power draw \u2014 specify rack power and cooling capacity; liquid-cooling available on request. <em>(Exact PSU rating, BTU, airflow, 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 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 1.5 TB DDR5 ECC \u00b7 30 TB NVMe \u00b7 4U rack<\/p>\n","protected":false},"featured_media":2253,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"DRACO 4\u00d7 H200 SXM GPU Server \u2014 2\u00d7 Intel Xeon 6, 1.5 TB DDR5 ECC, 564 GB HBM3e GPU | RDP GPU Mart","rank_math_description":"On-prem H200 SXM GPU server \u2014 4\u00d7 H200 SXM (564 GB HBM3e), 2\u00d7 Intel Xeon 6, 1.5 TB DDR5 ECC, 30 TB NVMe. Train & serve 180B-class models in your own data centre. Make-in-India, GST invoice, pan-India onsite. 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