{"id":12616,"date":"2026-08-18T16:15:30","date_gmt":"2026-08-18T16:15:30","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/quasar-4x-rtx-pro-6000-aidaptiv-ai-server\/"},"modified":"2026-08-18T23:24:41","modified_gmt":"2026-08-18T23:24:41","slug":"quasar-4x-rtx-pro-6000-aidaptiv-ai-server","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/quasar-4x-rtx-pro-6000-aidaptiv-ai-server\/","title":{"rendered":"QUASAR 4\u00d7 RTX PRO 6000 aiDAPTIV+ AI Server"},"content":{"rendered":"<p><strong>The first real server rung \u2014 four GPUs, multi-tenant, and still air-cooled.<\/strong> A 4U node with four NVIDIA RTX PRO 6000 Blackwell cards and an 8&nbsp;TB aiDAPTIV+ NVMe cache, built for teams that have outgrown a workstation but do not want liquid cooling, an NVSwitch fabric, or a datacenter retrofit.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>4\u00d7 NVIDIA RTX PRO 6000 Blackwell 96GB<\/strong> \u2014 384 GB aggregate VRAM in 4U.<\/li>\n<li><strong>8 TB aiDAPTIV+ cache<\/strong> \u2014 serves several large models concurrently rather than one at a time.<\/li>\n<li><strong>Dual Xeon<\/strong> host with <strong>512 GB DDR5 ECC<\/strong> for heavy data pipelines.<\/li>\n<li><strong>Multi-tenant serving<\/strong>: several teams or applications share one node with isolation.<\/li>\n<li><strong>Air-cooled 4U<\/strong> \u2014 fits a standard rack; no facility water, no CDU.<\/li>\n<li><strong>25\/100GbE<\/strong> networking for cluster-adjacent data movement.<\/li>\n<li>On-prem and <strong>DPDP-aligned<\/strong> \u2014 the data stays in your rack.<\/li>\n<\/ul>\n<h3>AI workload fit<\/h3>\n<ul>\n<li>Multi-tenant <strong>inference<\/strong> serving for 70B\u2013180B models.<\/li>\n<li><strong>Fine-tuning<\/strong> (LoRA\/QLoRA) as a shared department resource.<\/li>\n<li>Production <strong>RAG<\/strong> services with concurrent users.<\/li>\n<li><strong>Agentic AI<\/strong> back-ends running continuous workloads.<\/li>\n<li><strong>Computer vision<\/strong> and <strong>NLP &amp; speech<\/strong> at service scale.<\/li>\n<li>Consolidating several workstations onto one managed, monitored node.<\/li>\n<\/ul>\n<h3>AI workload positioning<\/h3>\n<p>This is where AI stops being a project and becomes a service. A workstation serves a person; this serves an organisation \u2014 with the concurrency, remote management and rack discipline that implies, while staying inside an ordinary air-cooled rack. 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>Enterprise &amp; GCCs:<\/strong> a shared inference service behind the corporate firewall.<\/li>\n<li><strong>BFSI:<\/strong> concurrent risk, fraud and document-intelligence services on-premises.<\/li>\n<li><strong>Healthcare:<\/strong> hospital-wide clinical AI services under residency rules.<\/li>\n<li><strong>Public sector &amp; sovereign:<\/strong> departmental AI platform on state-owned infrastructure.<\/li>\n<li><strong>Manufacturing:<\/strong> multi-line vision inference from a single plant-side node.<\/li>\n<\/ul>\n<h3>Performance &amp; how to be sure<\/h3>\n<p>Concurrency, not peak throughput, is the metric that matters here \u2014 how many simultaneous sessions hold their latency target while the aiDAPTIV+ cache keeps several models resident. 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 server tier. Below sit the DRACO workstations. Above: <strong>DRACO 8\u00d7 RTX PRO 6000<\/strong> (double the density), then <strong>H200<\/strong> and <strong>B200\/B300 SXM<\/strong> nodes with NVLink\/NVSwitch for true multi-GPU training, and rack-scale systems beyond that.<\/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. Standard serving stacks (vLLM, SGLang, TensorRT-LLM) are supported.<\/p>\n<h3>Power, thermal &amp; acoustics<\/h3>\n<p>4U air-cooled chassis with redundant power supplies; standard rack PDU feeds, no liquid cooling or CDU required \u2014 deployable in an existing server room. <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 6\u20138 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>Dual Xeon \u00b7 512GB DDR5 ECC \u00b7 8TB aiDAPTIV+ cache \u00b7 4U rack \u00b7 air-cooled<\/p>\n","protected":false},"featured_media":2028,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"QUASAR 4\u00d7 RTX PRO 6000 aiDAPTIV+ AI Server \u2014 4U On-Prem Inference | RDP GPU Mart","rank_math_description":"4U GPU server with four RTX PRO 6000 Blackwell and 8TB aiDAPTIV+ memory extension: multi-tenant 70B\u2013180B inference and fine-tuning on-premises. 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