{"id":75,"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-max\/"},"modified":"2026-07-06T01:47:53","modified_gmt":"2026-07-06T01:47:53","slug":"draco-4x-b200-sxm-gpu-server","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/draco-4x-b200-sxm-gpu-server\/","title":{"rendered":"DRACO 4\u00d7 B200 SXM GPU Server"},"content":{"rendered":"<p>The DRACO 4\u00d7 B200 SXM GPU Server is a Rack 4U rack server that brings NVIDIA&#8217;s Blackwell generation into your own data centre. Four NVIDIA B200 SXM (HGX B200 baseboard) GPUs deliver 720 GB HBM3e of HBM3e, linked by NVLink and NVSwitch into one tightly-coupled accelerator with Blackwell&#8217;s FP4\/FP8 throughput \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 adopting Blackwell on-prem, it pairs the four GPUs with a 2\u00d7 Intel Xeon 6 host, 2 TB DDR5 ECC and 30 TB NVMe, with 400G InfiniBand for scale-out, redundant power and full BMC\/IPMI \u2014 a liquid-cooled training node that racks and runs.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>720 GB HBM3e of HBM3e across 4\u00d7 B200 SXM<\/strong> \u2014 Blackwell-generation memory and bandwidth to train and serve up to 405B-class models.<\/li>\n<li><strong>NVLink + NVSwitch fabric<\/strong> \u2014 full all-to-all GPU bandwidth for efficient tensor-parallel training.<\/li>\n<li><strong>Blackwell FP4\/FP8<\/strong> \u2014 next-generation throughput for both training and high-efficiency inference.<\/li>\n<li><strong>2\u00d7 Intel Xeon 6 + 2 TB DDR5 ECC<\/strong> \u2014 high core count and memory bandwidth to feed four Blackwell GPUs.<\/li>\n<li><strong>Rack 4U, liquid-cooled, redundant PSU, BMC\/IPMI<\/strong> \u2014 sustained clocks, hot-swap drives, lights-out management.<\/li>\n<li><strong>30 TB NVMe + InfiniBand NDR 400G<\/strong> \u2014 fast local storage 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<\/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 training of 405B-class models, tensor- and pipeline-parallel across the four NVSwitch-linked Blackwell GPUs.<\/li>\n<li><strong>Inference:<\/strong> high-efficiency FP4\/FP8 serving of 405B-class models, or several large models concurrently.<\/li>\n<li><strong>RAG, vision, multimodal &amp; agentic:<\/strong> production pipelines on the 30 TB NVMe and multi-agent back-ends.<\/li>\n<li><em>Engineering note:<\/em> the four SXM GPUs sit on an HGX B200 baseboard with <strong>NVLink + NVSwitch<\/strong> \u2014 full all-to-all bandwidth for tensor-parallel models; Blackwell&#8217;s FP4 path is most valuable for inference efficiency and large-model serving.<\/li>\n<\/ul>\n<h3>AI workload positioning<\/h3>\n<p>This sits at the <strong>train-and-serve<\/strong> stage with Blackwell-generation efficiency. With 720 GB HBM3e of HBM3e on an NVLink+NVSwitch fabric and 400G InfiniBand, it is sized to <strong>sustain<\/strong> real large-model training and high-efficiency inference \u2014 a current-generation alternative to renting Blackwell cloud capacity around the clock.<\/p>\n<h3>Industry use cases<\/h3>\n<ul>\n<li><strong>BFSI<\/strong> \u2014 train and serve private large models 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 Blackwell-class AI on GeM-procurable infrastructure.<\/li>\n<li><strong>Neocloud \/ AI providers<\/strong> \u2014 a Blackwell building block for a GPU cloud.<\/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 a current-generation 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: 720 GB HBM3e of HBM3e across four NVSwitch-linked Blackwell 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 Blackwell within the line; step up to 8\u00d7 B200 or 8\u00d7 B300 HGX 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: four continuously-running Blackwell 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>A Rack 4U 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 Blackwell-generation 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 30 TB NVMe \u00b7 4U rack<\/p>\n","protected":false},"featured_media":2251,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"DRACO 4\u00d7 B200 SXM GPU Server \u2014 2\u00d7 Intel Xeon 6, 2 TB DDR5 ECC, 720 GB HBM3e GPU | RDP GPU Mart","rank_math_description":"On-prem Blackwell HGX server \u2014 4\u00d7 B200 SXM (720 GB HBM3e), 2\u00d7 Intel Xeon 6, 2 TB DDR5 ECC, 30 TB NVMe, NVLink+NVSwitch. Train & serve up to 405B on-prem. Make-in-India, GST invoice, pan-India onsite. 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