{"id":12615,"date":"2026-08-18T16:15:28","date_gmt":"2026-08-18T16:15:28","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/draco-4x-rtx-pro-6000-aidaptiv-workstation\/"},"modified":"2026-08-18T23:24:41","modified_gmt":"2026-08-18T23:24:41","slug":"draco-4x-rtx-pro-6000-aidaptiv-workstation","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/draco-4x-rtx-pro-6000-aidaptiv-workstation\/","title":{"rendered":"DRACO 4\u00d7 RTX PRO 6000 aiDAPTIV+ Workstation"},"content":{"rendered":"<p><strong>384 GB of VRAM in a tower &mdash; the largest models an organisation can run without building a server room.<\/strong> Four NVIDIA RTX PRO 6000 Blackwell cards, 1&nbsp;TB of ECC memory and a 2&nbsp;TB aiDAPTIV+ NVMe cache take the 180B+ class on-premises, entirely under your own control.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>4\u00d7 NVIDIA RTX PRO 6000 Blackwell 96GB<\/strong> \u2014 <strong>384 GB aggregate VRAM<\/strong>.<\/li>\n<li><strong>2 TB aiDAPTIV+ cache<\/strong> extends usable model memory beyond the 384 GB of silicon.<\/li>\n<li><strong>180B+ fine-tuning and inference<\/strong> \u2014 a class most organisations assume requires a datacenter node.<\/li>\n<li><strong>1 TB DDR5 ECC RDIMM<\/strong> (8\u00d7 128GB) on Intel Xeon w9 \u2014 no data-prep bottleneck.<\/li>\n<li><strong>8 TB NVMe<\/strong> primary storage for large corpora and full checkpoint lineage.<\/li>\n<li>Model-parallel across four cards; <strong>no NVSwitch required<\/strong> for this workload class.<\/li>\n<li>Air-cooled tower: <strong>no rack, no chilled water, no data-centre build<\/strong>.<\/li>\n<\/ul>\n<h3>AI workload fit<\/h3>\n<ul>\n<li><strong>Fine-tuning<\/strong> 180B+ models with LoRA\/QLoRA on proprietary corpora.<\/li>\n<li>High-quality production <strong>inference<\/strong> for mission-critical applications.<\/li>\n<li>Enterprise-scale <strong>RAG<\/strong> with long context windows.<\/li>\n<li>Complex <strong>agentic AI<\/strong> and multi-step reasoning workloads.<\/li>\n<li><strong>Sovereign AI<\/strong> work requiring full air-gap capability.<\/li>\n<li><em>Not<\/em> a substitute for a multi-node cluster on frontier pretraining.<\/li>\n<\/ul>\n<h3>AI workload positioning<\/h3>\n<p>The ceiling of the desk-side ladder and the last rung before a GPU server. Choose this when model quality matters more than multi-tenant throughput, and when the data cannot leave the building at any price. 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>Defence &amp; aerospace:<\/strong> air-gapped large-model development on classified material.<\/li>\n<li><strong>Public sector &amp; sovereign:<\/strong> national-language and policy models held entirely on-soil.<\/li>\n<li><strong>BFSI &amp; HFT:<\/strong> large proprietary research and risk models with zero external exposure.<\/li>\n<li><strong>Healthcare:<\/strong> multimodal clinical AI on protected patient data.<\/li>\n<li><strong>Research &amp; education:<\/strong> frontier-adjacent research without cluster allocation politics.<\/li>\n<\/ul>\n<h3>Performance &amp; how to be sure<\/h3>\n<p>With 384 GB VRAM plus a 2 TB cache, capacity stops being the limit and precision\/parallelism strategy becomes the design question \u2014 exactly the thing a paper spec cannot answer for you. 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>Top of the <strong>DRACO<\/strong> workstation line. Below: <strong>DRACO 2\u00d7 RTX PRO 6000<\/strong> (70B\u2013180B). Above: RDP GPU servers \u2014 <strong>QUASAR 4\u00d7 RTX PRO 6000<\/strong>, <strong>DRACO 8\u00d7 RTX PRO 6000<\/strong>, then H200 and B200\/B300 SXM nodes for multi-tenant serving 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. Standard serving stacks (vLLM, SGLang, TensorRT-LLM) are supported.<\/p>\n<h3>Power, thermal &amp; acoustics<\/h3>\n<p>Four high-TDP GPUs in a tower chassis \u2014 a dedicated high-capacity 230V circuit and a server-room location with proper airflow are required, not optional, 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 6 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 1TB DDR5 ECC \u00b7 2TB aiDAPTIV+ cache \u00b7 8TB NVMe \u00b7 Tower<\/p>\n","protected":false},"featured_media":1992,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"DRACO 4\u00d7 RTX PRO 6000 aiDAPTIV+ Workstation \u2014 180B+ On-Prem AI | RDP GPU Mart","rank_math_description":"Four RTX PRO 6000 Blackwell GPUs, 384GB VRAM, 2TB aiDAPTIV+ memory extension and 1TB ECC: 180B+ model fine-tuning on-premises. Request a quote from RDP GPU Mart.","_hermes_jsonld":""},"product_brand":[],"product_cat":[21],"product_tag":[],"class_list":["post-12615","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-flagship-aidaptiv-workstation-fine-tune","first","instock","taxable","shipping-taxable","product-type-external"],"_links":{"self":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product\/12615","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=12615"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media\/1992"}],"wp:attachment":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media?parent=12615"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_brand?post=12615"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_cat?post=12615"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_tag?post=12615"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}