{"id":93,"date":"2026-06-14T16:55:58","date_gmt":"2026-06-14T16:55:58","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/rdp-aix-2900-dual-gpu-workstation-max\/"},"modified":"2026-07-06T01:47:39","modified_gmt":"2026-07-06T01:47:39","slug":"draco-4x-rtx-pro-5000-blackwell-ai-workstation","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/draco-4x-rtx-pro-5000-blackwell-ai-workstation\/","title":{"rendered":"DRACO 4\u00d7 RTX PRO 5000 Blackwell AI Workstation"},"content":{"rendered":"<p>The DRACO 4\u00d7 RTX PRO 5000 Blackwell AI Workstation is RDP&#8217;s mid-flagship desk-side machine \u2014 four NVIDIA RTX PRO 5000 Blackwell GPUs and 192 GB of next-generation GDDR7 in a single quiet, liquid-cooled tower, sized to run a 70B model in FP16 on-premises. It puts a private alternative to four rented high-memory cloud GPUs under one desk: your models and data never leave the building, costs are fixed in INR, and the box is productive on day one.<\/p>\n<p>Built for AI\/ML teams, research labs and product-engineering groups standardising on a repeatable, secure local-AI platform, it balances 192 GB of aggregate Blackwell GPU memory, a high-core Intel Xeon W-3500 data-prep engine, 512 GB of ECC system memory and 8 TB of NVMe \u2014 so the GPUs stay fed for serious training and serving.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>192 GB of GPU memory across 4\u00d7 RTX PRO 5000 Blackwell<\/strong> \u2014 run a 70B model in FP16 or fine-tune large models locally; serve several models at once without queueing for cloud capacity.<\/li>\n<li><strong>Blackwell architecture with FP4 support<\/strong> \u2014 next-gen inference efficiency, ECC throughout for stable long fine-tune runs.<\/li>\n<li><strong>Intel Xeon W-3500 + 512 GB DDR5 ECC<\/strong> \u2014 a high-core data-prep, tokenisation and orchestration engine so the four GPUs aren&#8217;t starved.<\/li>\n<li><strong>8 TB NVMe + 25 GbE<\/strong> \u2014 fast local datasets, checkpoints and weights, with high-speed networking for multi-node experiments; no egress fees.<\/li>\n<li><strong>Liquid-cooled, desk-side<\/strong> \u2014 sustained all-GPU clocks under load, quiet enough for an office, not a server room.<\/li>\n<li><strong>On-prem data sovereignty<\/strong> \u2014 IP and customer data 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>Upgrade path<\/strong> \u2014 step up within DRACO to 4\u00d7 RTX PRO 6000 Blackwell (384 GB), or to a rack-scale RDP system when you outgrow four GPUs.<\/li>\n<\/ul>\n<h3>AI workload fit (what it actually runs \u2014 honestly)<\/h3>\n<ul>\n<li><strong>Full-precision large models:<\/strong> a 70B model in FP16 (~140 GB) runs <strong>tensor-parallel across all four cards<\/strong> using the 192 GB aggregate pool.<\/li>\n<li><strong>Inference:<\/strong> serve 70B-class models, or run several quantised 7B\u201334B models concurrently across the four GPUs.<\/li>\n<li><strong>Fine-tuning:<\/strong> QLoRA \/ LoRA up to ~70B, and full fine-tuning of 7B\u201313B models, data-parallel across the four GPUs.<\/li>\n<li><strong>Vision, multimodal, RAG &amp; agentic:<\/strong> train\/serve vision and multimodal models, build RAG pipelines on the 8 TB NVMe, and run multi-agent workflows locally.<\/li>\n<li><em>Engineering note:<\/em> the RTX PRO 5000 Blackwell workstation card has <strong>no NVLink<\/strong>, so the four cards run over PCIe \u2014 use <strong>tensor parallelism<\/strong> for one model larger than 48 GB and <strong>data parallelism<\/strong> for multi-model serving. This is the right tool for fine-tuning + inference at team scale, not 1000-GPU pre-training.<\/li>\n<\/ul>\n<h3>AI workload positioning<\/h3>\n<p>This sits at the <strong>fine-tune-and-deploy<\/strong> stage of the AI lifecycle, at the high end of desk-side capability: lighter and far cheaper to own than a rack-scale cluster, but with enough GPU memory to handle 70B-class models on-prem. With 192 GB of Blackwell GPU memory, a high-core Xeon W, ECC memory and fast NVMe in balance, it is built to <strong>sustain<\/strong> serious fine-tuning and high-throughput inference.<\/p>\n<h3>Industry use cases<\/h3>\n<ul>\n<li><strong>Manufacturing<\/strong> \u2014 defect-detection vision models and digital-twin simulation on the factory floor.<\/li>\n<li><strong>Healthcare &amp; life sciences<\/strong> \u2014 fine-tune medical LLMs \/ imaging models on-prem, keeping PHI in-house.<\/li>\n<li><strong>BFSI<\/strong> \u2014 private fraud, risk and document-intelligence models under data-residency rules.<\/li>\n<li><strong>Media &amp; design<\/strong> \u2014 generative image\/video and 3D\/rendering pipelines.<\/li>\n<li><strong>Research &amp; higher-ed<\/strong> \u2014 a shared lab AI workstation for NLP, speech and multimodal research.<\/li>\n<li><strong>Software \/ product teams<\/strong> \u2014 local fine-tuning, eval and agentic-app development without cloud bills.<\/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: 192 GB of aggregate Blackwell GPU memory and four GPUs are sized to fine-tune up to ~70B (QLoRA) and serve 70B-class models in FP16 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 fine-tune timings for your workload.<\/p>\n<h3>Series &amp; upgrade path<\/h3>\n<ul>\n<li><strong>CARINA<\/strong> (entry, 1\u00d7 GPU) \u00b7 <strong>QUASAR<\/strong> (performance, 2\u00d7 GPU) \u00b7 <strong>DRACO<\/strong> (flagship, 4\u00d7 GPU \u2014 <em>this<\/em>).<\/li>\n<li><strong>Within DRACO:<\/strong> step up to 4\u00d7 RTX PRO 6000 Blackwell (384 GB) for the largest on-prem models and maximum throughput.<\/li>\n<li><strong>When to step up:<\/strong> beyond four GPUs or for trillion-parameter training, move to an RDP rack-scale GPU server \/ NVL-class system \u2014 talk to an architect for the migration path.<\/li>\n<\/ul>\n<h3>On-prem vs cloud \u2014 the TCO case<\/h3>\n<p>For sustained fine-tuning and inference, owning beats renting: four equivalent high-memory cloud GPUs running continuously add up fast, and on-prem removes egress fees and keeps data 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-loaded and ready to train<\/strong>: NVIDIA driver, CUDA, cuDNN, Docker and NVIDIA Container Toolkit, with PyTorch \/ TensorFlow and common inference servers (vLLM \/ Triton) configured on Ubuntu LTS. Optional managed AI-stack and model-zoo setup available.<\/p>\n<h3>Power, thermal &amp; acoustics<\/h3>\n<p>Four GPUs plus the Xeon W-3500 draw substantial power \u2014 specify a dedicated circuit. The system is <strong>liquid-cooled<\/strong> to hold full clocks quietly at the desk. <em>(Exact PSU rating, BTU and dB(A) figures confirmed on the build sheet.)<\/em><\/p>\n<h3>Deployment, warranty &amp; support<\/h3>\n<ul>\n<li><strong>Made to order<\/strong>, built and burned-in in India; realistic lead time confirmed at quote.<\/li>\n<li><strong>In the box:<\/strong> workstation, power cables, feet, quick-start, and the pre-installed AI software stack.<\/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 flagship, made-to-order system \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>Intel Xeon W-3500 \u00b7 512 GB DDR5 ECC \u00b7 8 TB NVMe \u00b7 Liquid-cooled 4-GPU tower<\/p>\n","protected":false},"featured_media":1996,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"DRACO 4\u00d7 RTX PRO 5000 Blackwell AI Workstation \u2014 Xeon W, 512GB ECC, 192GB GPU | RDP GPU Mart","rank_math_description":"Flagship 4-GPU desk-side AI workstation \u2014 4\u00d7 RTX PRO 5000 Blackwell (192 GB), Xeon W-3500, 512 GB ECC, 8 TB NVMe, liquid-cooled. Run 70B in FP16 on-prem. Make-in-India, GST invoice, pan-India onsite. 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