{"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":"2026-08-30T15:12:54","modified_gmt":"2026-08-30T15:12:54","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, 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, 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 | 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 and serve 180B models. Made in India.","_hermes_jsonld":""},"product_brand":[],"product_cat":[18],"product_tag":[83],"class_list":["post-74","product","type-product","status-publish","has-post-thumbnail","product_cat-gpu-servers","product_tag-ready-to-buy","pa_form-factor-rack","pa_gpu-model-nvidia-h200-sxm5","pa_industry-automotive-mobility","pa_industry-bfsi-hft","pa_industry-defence-aerospace","pa_industry-enterprise-gccs","pa_industry-healthcare-life-sciences","pa_industry-manufacturing-industrial","pa_industry-media-gaming-entertainment","pa_industry-neocloud","pa_industry-public-sector-sovereign-ai","pa_industry-research-higher-education","pa_industry-retail-ecommerce","pa_industry-telecom-5g","pa_series-draco","pa_use-case-agentic-ai","pa_use-case-computer-vision","pa_use-case-fine-tuning","pa_use-case-generative-ai","pa_use-case-inference","pa_use-case-llm-training","pa_use-case-nlp-speech","pa_use-case-rag","pa_workload-fit-tensor-parallel-training","first","onbackorder","taxable","shipping-taxable","product-type-simple"],"_links":{"self":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product\/74","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/types\/product"}],"replies":[{"embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/comments?post=74"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media\/2253"}],"wp:attachment":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media?parent=74"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_brand?post=74"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_cat?post=74"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_tag?post=74"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}