The DRACO 64\u00d7 H200 Multi-Node System is a dense 16-blade chassis system that packs 64 NVIDIA H200 SXM5 GPUs (9,024 GB HBM3e) into one shared-infrastructure chassis with pooled power and liquid cooling. It consolidates large-scale training and high-throughput inference into one efficient, serviceable footprint on-premises, in INR, on a GST invoice.<\/p>\n
Engineered for platform teams who want training-grade density, it shares power, cooling and management across the blades, with NVLink inside each blade and a non-blocking InfiniBand fabric between them \u2014 delivered racked, cabled and validated as a single SKU with one warranty.<\/p>\n
This sits at the train-and-serve at density<\/strong> stage: a density play that puts many training-grade GPUs in one serviceable chassis. With 9,024 GB HBM3e across 64 GPUs, it is sized to sustain<\/strong> distributed training and large-scale serving in one footprint \u2014 more efficient than many separate servers, simpler than a full rack-scale build.<\/p>\n We don’t publish inflated peak numbers. The honest picture: 9,024 GB HBM3e across 64 GPUs in one shared chassis is sized for distributed training and high-throughput serving. Want certainty? Request a free benchmark of your model and dataset, including a scaling test, on this exact configuration before you buy<\/strong>; we’ll send back real tokens\/sec, scaling efficiency and density figures.<\/p>\n For sustained training-grade density, owning beats renting: a continuously-running cloud GPU fleet of this size dominates an AI budget, and on-prem removes egress and keeps data and weights in-house. Shared power\/cooling lowers per-GPU operating cost. RDP pricing is fixed in INR with a GST input-credit-eligible invoice \u2014 ask for a 3-year TCO comparison<\/strong>.<\/p>\n Ships pre-configured to train and serve<\/strong>: NVIDIA driver, CUDA, cuDNN, NCCL, the InfiniBand stack, Docker and NVIDIA Container Toolkit, with Kubernetes\/Slurm, PyTorch and vLLM \/ Triton \/ TensorRT-LLM on Ubuntu LTS. Optional managed operations and observability.<\/p>\n A Dual Blade Chassis 16U liquid-cooled system with shared, redundant PSUs \u2014 plan rack power, cooling and the InfiniBand fabric. (Exact PSU rating, BTU, flow and rack-depth figures confirmed on the build sheet.)<\/em> Single management plane via BMC\/IPMI across the blades.<\/p>\n 14 years of Make-in-India infrastructure and 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 This is a dense, training-grade multi-node GPU system, made to order \u2014 talk to an RDP solution architect<\/strong>, size the density, fabric and cooling for your workload, get a 3-year TCO, and benchmark your own model before you commit.<\/strong> Request a quote to begin.<\/p>\n","protected":false},"excerpt":{"rendered":" 16-blade chassis \u00b7 32\u00d7 Intel Xeon 6 (16 blades) \u00b7 16 TB DDR5 ECC \u00b7 320 TB NVMe \u00b7 Dual Blade Chassis 16U \u00b7 liquid-cooled<\/p>\n","protected":false},"featured_media":2119,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"DRACO 64\u00d7 H200 Multi-Node System \u2014 9,024 GB HBM3e, 16-blade chassis | RDP GPU Mart","rank_math_description":"Dense on-prem training-grade multi-node GPU system \u2014 64\u00d7 H200 (9,024 GB HBM3e), 16-blade chassis, NVLink + InfiniBand, liquid-cooled. Make-in-India, GST invoice. Request a quote.","_hermes_jsonld":""},"product_brand":[],"product_cat":[27],"product_tag":[83],"class_list":["post-128","product","type-product","status-publish","has-post-thumbnail","product_cat-multi-node-blade","product_tag-ready-to-buy","pa_form-factor-rack","pa_gpu-model-nvidia-h200-sxm5","pa_industry-neocloud","pa_industry-public-sector-sovereign-ai","pa_industry-research-higher-education","pa_industry-telecom-5g","pa_series-draco","pa_use-case-agentic-ai","pa_use-case-computer-vision","pa_use-case-generative-ai","pa_use-case-hpc-ai","pa_use-case-inference","pa_use-case-llm-training","pa_use-case-fine-tuning","pa_use-case-nlp-speech","pa_use-case-rag","pa_workload-fit-dense-multi-node-training-inference","first","onbackorder","taxable","shipping-taxable","product-type-simple"],"_links":{"self":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product\/128","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=128"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media\/2119"}],"wp:attachment":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media?parent=128"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_brand?post=128"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_cat?post=128"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_tag?post=128"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Industry use cases<\/h3>\n
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Performance \u2014 and how to be sure<\/h3>\n
Series & upgrade path<\/h3>\n
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On-prem vs cloud \u2014 the TCO case<\/h3>\n
Software & day-one readiness<\/h3>\n
Power, cooling & rack integration<\/h3>\n
Deployment, warranty & support<\/h3>\n
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Why RDP<\/h3>\n
Buy with confidence<\/h3>\n