{"id":12610,"date":"2026-08-18T16:11:33","date_gmt":"2026-08-18T16:11:33","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/carina-1x-rtx-pro-5000-aidaptiv-workstation\/"},"modified":"2026-08-18T23:24:41","modified_gmt":"2026-08-18T23:24:41","slug":"carina-1x-rtx-pro-5000-aidaptiv-workstation","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/carina-1x-rtx-pro-5000-aidaptiv-workstation\/","title":{"rendered":"CARINA 1\u00d7 RTX PRO 5000 aiDAPTIV+ Workstation"},"content":{"rendered":"<p><strong>The entry point to serious on-premises AI &mdash; one GPU, models that normally need several.<\/strong> A single NVIDIA RTX PRO 5000 Blackwell paired with a 640&nbsp;GB aiDAPTIV+ NVMe cache lets a developer fine-tune and serve 13B&ndash;34B models at their own desk, behind the firewall, with no cloud GPU meter running.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>1\u00d7 NVIDIA RTX PRO 5000 Blackwell 48GB GDDR7<\/strong> \u2014 professional-class compute with ECC throughout.<\/li>\n<li><strong>640 GB aiDAPTIV+ cache<\/strong> (2\u00d7 M.2 enterprise NVMe) \u2014 extends usable model memory well past the 48 GB of on-card VRAM.<\/li>\n<li><strong>Fine-tune 13B\u201334B locally<\/strong> with LoRA\/QLoRA \u2014 the class of work most Indian enterprises actually start with.<\/li>\n<li><strong>128 GB DDR5 ECC RDIMM<\/strong> \u2014 headroom for data preparation and multi-model experimentation.<\/li>\n<li><strong>2 TB NVMe<\/strong> primary storage for datasets, checkpoints and container images.<\/li>\n<li>Runs on a <strong>standard office circuit<\/strong> \u2014 no rack, no dedicated cooling, no facility work.<\/li>\n<li><strong>Your data never leaves the building<\/strong> \u2014 DPDP-aligned by architecture, not by policy promise.<\/li>\n<\/ul>\n<h3>AI workload fit<\/h3>\n<ul>\n<li><strong>Fine-tuning<\/strong> 13B\u201334B models with LoRA\/QLoRA on proprietary data.<\/li>\n<li><strong>Inference<\/strong> and model serving for a team or department.<\/li>\n<li><strong>RAG<\/strong> pipelines over private document sets.<\/li>\n<li><strong>Agentic AI<\/strong> development and iteration.<\/li>\n<li><strong>Computer vision<\/strong> and <strong>NLP &amp; speech<\/strong> prototyping.<\/li>\n<li><em>Not<\/em> for large-scale pretraining \u2014 that is cluster work (see the upgrade path).<\/li>\n<\/ul>\n<h3>AI workload positioning<\/h3>\n<p>This is the first rung of the ladder: the machine an organisation buys when cloud GPU spend has become predictable enough to own instead of rent, but a multi-GPU server is not yet justified. 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> fine-tune on transaction and policy data that cannot leave the premises under DPDP.<\/li>\n<li><strong>Healthcare:<\/strong> local work on patient records and clinical notes with residency preserved.<\/li>\n<li><strong>Enterprise &amp; GCCs:<\/strong> a private development box per AI engineer instead of contended cloud credits.<\/li>\n<li><strong>Research &amp; education:<\/strong> per-lab AI compute inside a departmental budget.<\/li>\n<li><strong>Manufacturing:<\/strong> defect-detection and quality-inspection model development close to the line.<\/li>\n<\/ul>\n<h3>Performance &amp; how to be sure<\/h3>\n<p>Capability here is set by aiDAPTIV+ memory extension rather than raw FLOPS: the constraint that normally stops a 34B model running on one card is capacity, and that is precisely what the NVMe cache addresses. 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>CARINA<\/strong> is the entry tier. When one GPU is no longer enough: step to the <strong>QUASAR 2\u00d7 RTX PRO 4500<\/strong> (34B\u201370B), then <strong>QUASAR 2\u00d7 RTX PRO 5000<\/strong> (70B), then the <strong>DRACO<\/strong> workstations (90B\u2013180B), and finally an RDP GPU server. The CUDA and aiDAPTIV+ software stack is identical at every rung, so nothing is rewritten when you scale.<\/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>Single-socket tower on a standard 230V office circuit, air-cooled, sized for a desk-side or small server-room location. <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 w5 \u00b7 128GB DDR5 ECC \u00b7 640GB aiDAPTIV+ cache \u00b7 2TB NVMe \u00b7 Tower<\/p>\n","protected":false},"featured_media":1999,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"CARINA 1\u00d7 RTX PRO 5000 aiDAPTIV+ AI Workstation \u2014 13B\u201334B On-Prem | RDP GPU Mart","rank_math_description":"Entry AI workstation with aiDAPTIV+ NVMe memory extension: run and fine-tune 13B\u201334B models on one RTX PRO 5000, on-premises. 128GB ECC, 640GB cache. 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