{"id":12613,"date":"2026-08-18T16:13:33","date_gmt":"2026-08-18T16:13:33","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/draco-2x-rtx-pro-6000-aidaptiv-workstation\/"},"modified":"2026-08-18T23:24:41","modified_gmt":"2026-08-18T23:24:41","slug":"draco-2x-rtx-pro-6000-aidaptiv-workstation","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/draco-2x-rtx-pro-6000-aidaptiv-workstation\/","title":{"rendered":"DRACO 2\u00d7 RTX PRO 6000 aiDAPTIV+ Workstation"},"content":{"rendered":"<p><strong>Server-class model sizes without a server room.<\/strong> Two NVIDIA RTX PRO 6000 Blackwell cards &mdash; 192&nbsp;GB aggregate VRAM &mdash; backed by a 2&nbsp;TB aiDAPTIV+ NVMe cache and 512&nbsp;GB of ECC memory, reaching the 70B&ndash;180B band that normally sends organisations shopping for an eight-GPU node.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>2\u00d7 NVIDIA RTX PRO 6000 Blackwell 96GB<\/strong> \u2014 <strong>192 GB aggregate VRAM<\/strong>, the most memory available in a professional tower.<\/li>\n<li><strong>2 TB aiDAPTIV+ cache<\/strong> (2\u00d7 1TB U.2 enterprise NVMe) \u2014 reaches the 180B band.<\/li>\n<li><strong>512 GB DDR5 ECC RDIMM<\/strong> on Intel Xeon w9 \u2014 large-corpus preparation without spilling.<\/li>\n<li><strong>8 TB NVMe<\/strong> primary storage for multi-model estates and checkpoint history.<\/li>\n<li><strong>70B\u2013180B<\/strong> fine-tuning and inference on two cards rather than eight.<\/li>\n<li>Still a <strong>tower<\/strong>: air-cooled, standard power, no rack or chilled-water dependency.<\/li>\n<li>Sovereign by construction \u2014 <strong>nothing leaves the premises<\/strong>, DPDP-aligned.<\/li>\n<\/ul>\n<h3>AI workload fit<\/h3>\n<ul>\n<li><strong>Fine-tuning<\/strong> 70B\u2013180B models (LoRA\/QLoRA) on private data.<\/li>\n<li>High-quality <strong>inference<\/strong> for business-critical applications.<\/li>\n<li>Large-scale <strong>RAG<\/strong> across an enterprise document estate.<\/li>\n<li>Advanced <strong>agentic AI<\/strong> with long context and tool use.<\/li>\n<li><strong>Generative AI<\/strong>, <strong>computer vision<\/strong>, <strong>NLP &amp; speech<\/strong> at production quality.<\/li>\n<li>Also viable as a <strong>pre-production staging box<\/strong> ahead of a GPU-server rollout.<\/li>\n<\/ul>\n<h3>AI workload positioning<\/h3>\n<p>The top of the desk-side ladder, and the honest alternative to a first GPU server for organisations that need model quality but not multi-tenant throughput. aiDAPTIV+ extends usable model memory onto high-endurance enterprise NVMe, so a model class that would normally demand far more GPUs runs on the GPUs you actually own. It is a <strong>capacity<\/strong> technology, not a bandwidth one &mdash; streaming offload is tuned for inference and parameter-efficient fine-tuning (LoRA\/QLoRA), and the final numbers are confirmed in a scoped proof-of-concept, not promised on a datasheet.<\/p>\n<h3>Industry use cases<\/h3>\n<ul>\n<li><strong>BFSI &amp; HFT:<\/strong> large document-intelligence and research models on-premises.<\/li>\n<li><strong>Defence &amp; aerospace:<\/strong> air-gap-capable model development on classified data.<\/li>\n<li><strong>Public sector &amp; sovereign:<\/strong> departmental large-model capability under DPDP.<\/li>\n<li><strong>Healthcare:<\/strong> multimodal clinical models on protected patient data.<\/li>\n<li><strong>Research &amp; education:<\/strong> principal-investigator-scale compute without cluster contention.<\/li>\n<\/ul>\n<h3>Performance &amp; how to be sure<\/h3>\n<p>192 GB of aggregate VRAM plus a 2 TB NVMe cache moves the ceiling from &#8220;which model fits&#8221; to &#8220;which model you want&#8221; \u2014 but throughput at 180B depends heavily on precision, context length and offload ratio. Rather than quote a tokens\/sec figure that will not match your workload, RDP offers a <strong>&ldquo;benchmark your model&rdquo;<\/strong> session: bring the model, precision and context length you actually intend to run, and we will validate it on this exact configuration before you commit.<\/p>\n<h3>Series &amp; upgrade path<\/h3>\n<p><strong>DRACO<\/strong> is the flagship tier. Below: <strong>QUASAR 2\u00d7 RTX PRO 5000<\/strong> (70B). Above: <strong>DRACO 4\u00d7 RTX PRO 6000<\/strong> (180B+), then RDP GPU servers (4\u00d7 and 8\u00d7 RTX PRO 6000, H200, B200\/B300) for multi-tenant throughput and true multi-GPU training. Identical software stack throughout.<\/p>\n<h3>On-prem vs cloud (TCO)<\/h3>\n<p>For sustained daily AI work this configuration removes per-GPU-hour billing, queueing for scarce instances, and egress charges on your own data &mdash; and keeps everything on-premises for DPDP and data-residency obligations. Cloud still wins for burst capacity and one-off very large training runs; on-prem wins on sustained utilisation, control and predictable INR capital cost. We model the crossover with your actual usage rather than assert it.<\/p>\n<h3>Software &amp; day-one readiness<\/h3>\n<p>Ships workload-ready: Ubuntu LTS or RHEL, NVIDIA driver + CUDA + cuDNN, container runtime, and the aiDAPTIV+ memory-management layer, which is PyTorch-compliant and needs <strong>no changes to your model code<\/strong>. The aiDAPTIVPro toolchain covers data ingest, RAG, fine-tune, monitor, validate and inference from one interface, so a team is productive on day one rather than week three.<\/p>\n<h3>Power, thermal &amp; acoustics<\/h3>\n<p>Dual high-TDP GPUs in a tower chassis; a dedicated 230V circuit and a server-room or well-ventilated location are recommended for sustained runs. <em>Exact wattage, BTU and dB(A) figures come from RDP bench measurement rather than estimates &mdash; ask for the site-readiness sheet with your quote.<\/em><\/p>\n<h3>Deployment, warranty &amp; support<\/h3>\n<p>Built to order by RDP Technologies. Supplied with GST invoice (HSN 8471), pan-India onsite support, and availability through GeM for public-sector procurement. Built to order \u2014 typically 4\u20136 weeks.<\/p>\n<h3>Why RDP<\/h3>\n<p>RDP Technologies is a Make-in-India OEM with 14+ years and 300,000+ units shipped, supplying AI infrastructure from desk-side systems to rack-scale AI factories &mdash; with predictable INR pricing, GST invoicing and pan-India onsite service.<\/p>\n<h3>Buy with confidence<\/h3>\n<p>Use <strong>Request a Quote<\/strong> to reach an RDP solution architect for sizing, a benchmark-your-model session, financing options and a delivery plan. No obligation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Intel Xeon w9 \u00b7 512GB DDR5 ECC \u00b7 2TB aiDAPTIV+ cache \u00b7 8TB NVMe \u00b7 Tower<\/p>\n","protected":false},"featured_media":1993,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"DRACO 2\u00d7 RTX PRO 6000 aiDAPTIV+ AI Workstation \u2014 70B\u2013180B On-Prem | RDP GPU Mart","rank_math_description":"Flagship dual RTX PRO 6000 workstation with 2TB aiDAPTIV+ memory extension: 70B\u2013180B fine-tuning and inference on-premises. 512GB ECC, Xeon w9. Request a quote from RDP.","_hermes_jsonld":""},"product_brand":[],"product_cat":[21],"product_tag":[],"class_list":["post-12613","product","type-product","status-publish","has-post-thumbnail","product_cat-ai-workstations","pa_form-factor-tower","pa_gpu-model-nvidia-rtx-pro-6000-blackwell","pa_industry-bfsi-hft","pa_industry-defence-aerospace","pa_industry-healthcare-life-sciences","pa_industry-public-sector-sovereign-ai","pa_industry-research-higher-education","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-nlp-speech","pa_use-case-rag","pa_use-case-sovereign-ai","pa_workload-fit-large-model-aidaptiv-fine-tune","first","instock","taxable","shipping-taxable","product-type-external"],"_links":{"self":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product\/12613","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=12613"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media\/1993"}],"wp:attachment":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media?parent=12613"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_brand?post=12613"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_cat?post=12613"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_tag?post=12613"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}