{"id":12612,"date":"2026-08-18T16:13:32","date_gmt":"2026-08-18T16:13:32","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/quasar-2x-rtx-pro-5000-aidaptiv-workstation\/"},"modified":"2026-08-18T23:24:41","modified_gmt":"2026-08-18T23:24:41","slug":"quasar-2x-rtx-pro-5000-aidaptiv-workstation","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/quasar-2x-rtx-pro-5000-aidaptiv-workstation\/","title":{"rendered":"QUASAR 2\u00d7 RTX PRO 5000 aiDAPTIV+ Workstation"},"content":{"rendered":"<p><strong>70B-class models, fine-tuned at your desk, on two GPUs.<\/strong> Dual NVIDIA RTX PRO 5000 Blackwell cards (96&nbsp;GB aggregate) with a 1.32&nbsp;TB aiDAPTIV+ NVMe cache put the 70B tier &mdash; the size most enterprises standardise on once they leave pilots &mdash; inside a tower you own, power from a normal circuit, and never send to a cloud.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>2\u00d7 NVIDIA RTX PRO 5000 Blackwell 48GB<\/strong> \u2014 96 GB aggregate VRAM.<\/li>\n<li><strong>1.32 TB aiDAPTIV+ cache<\/strong> (1TB U.2 + 320GB M.2 enterprise NVMe).<\/li>\n<li><strong>70B fine-tuning<\/strong> (LoRA\/QLoRA) plus BF16 inference \u2014 without a GPU server.<\/li>\n<li><strong>256 GB DDR5 ECC RDIMM<\/strong> on Intel Xeon w7 for heavy data preparation.<\/li>\n<li><strong>4 TB NVMe<\/strong> primary storage for corpora, checkpoints and model versions.<\/li>\n<li>Tower form factor, air-cooled \u2014 <strong>no rack, no facility chilled water<\/strong>.<\/li>\n<li>Full <strong>data sovereignty<\/strong>: training and inference stay behind your firewall.<\/li>\n<\/ul>\n<h3>AI workload fit<\/h3>\n<ul>\n<li><strong>Fine-tuning<\/strong> 70B-class models with LoRA\/QLoRA on proprietary data.<\/li>\n<li>Departmental <strong>inference<\/strong> and model serving at BF16\/INT4.<\/li>\n<li>Large-corpus <strong>RAG<\/strong> with private embeddings.<\/li>\n<li><strong>Agentic AI<\/strong> pipelines and tool-using assistants.<\/li>\n<li><strong>Generative AI<\/strong>, <strong>computer vision<\/strong>, <strong>NLP &amp; speech<\/strong>.<\/li>\n<li><em>Not<\/em> for pretraining a foundation model from scratch.<\/li>\n<\/ul>\n<h3>AI workload positioning<\/h3>\n<p>The point on the ladder where a workstation genuinely replaces a small server for most teams. 70B is where enterprise accuracy expectations are typically met, and aiDAPTIV+ is what makes that size reachable on two professional cards instead of an eight-GPU node. 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:<\/strong> 70B document-intelligence and risk models tuned on in-house records.<\/li>\n<li><strong>Healthcare:<\/strong> clinical summarisation and coding models kept fully on-premises.<\/li>\n<li><strong>Public sector &amp; sovereign:<\/strong> departmental AI with data-residency guarantees.<\/li>\n<li><strong>Research &amp; education:<\/strong> serious fine-tuning without queueing for shared cluster time.<\/li>\n<li><strong>Enterprise &amp; GCCs:<\/strong> the platform team&#8217;s reference box before scaling to servers.<\/li>\n<\/ul>\n<h3>Performance &amp; how to be sure<\/h3>\n<p>At this tier the binding constraint is memory capacity plus offload bandwidth, not raw TFLOPS \u2014 a 70B model at 4-bit behaves very differently from BF16, so a paper figure would mislead. 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>QUASAR<\/strong> is the performance tier. Below: <strong>QUASAR 2\u00d7 RTX PRO 4500<\/strong> (34B\u201370B) and <strong>CARINA 1\u00d7 RTX PRO 5000<\/strong> (13B\u201334B). Above: <strong>DRACO 2\u00d7 RTX PRO 6000<\/strong> (70B\u2013180B) and <strong>DRACO 4\u00d7 RTX PRO 6000<\/strong> (180B+), then RDP GPU servers. The CUDA + aiDAPTIV+ stack is identical at every rung.<\/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-GPU tower, air-cooled, single 230V circuit; server-room placement recommended for sustained multi-day fine-tuning 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 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 w7 \u00b7 256GB DDR5 ECC \u00b7 1.32TB aiDAPTIV+ cache \u00b7 4TB NVMe \u00b7 Tower<\/p>\n","protected":false},"featured_media":1994,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"QUASAR 2\u00d7 RTX PRO 5000 aiDAPTIV+ AI Workstation \u2014 70B On-Prem | RDP GPU Mart","rank_math_description":"Dual RTX PRO 5000 AI workstation with 1.32TB aiDAPTIV+ NVMe memory extension: fine-tune and serve 70B models on-premises under DPDP. Request a quote from RDP GPU Mart.","_hermes_jsonld":""},"product_brand":[],"product_cat":[21],"product_tag":[],"class_list":["post-12612","product","type-product","status-publish","has-post-thumbnail","product_cat-ai-workstations","pa_form-factor-tower","pa_gpu-model-nvidia-rtx-pro-5000-blackwell","pa_industry-bfsi-hft","pa_industry-enterprise-gccs","pa_industry-healthcare-life-sciences","pa_industry-public-sector-sovereign-ai","pa_industry-research-higher-education","pa_series-quasar","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_workload-fit-high-capacity-aidaptiv-fine-tune","first","instock","taxable","shipping-taxable","product-type-external"],"_links":{"self":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product\/12612","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=12612"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media\/1994"}],"wp:attachment":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media?parent=12612"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_brand?post=12612"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_cat?post=12612"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_tag?post=12612"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}