{"id":12619,"date":"2026-08-18T16:17:47","date_gmt":"2026-08-18T16:17:47","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/draco-8x-rtx-pro-6000-aidaptiv-ai-server\/"},"modified":"2026-08-18T23:24:41","modified_gmt":"2026-08-18T23:24:41","slug":"draco-8x-rtx-pro-6000-aidaptiv-ai-server","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/draco-8x-rtx-pro-6000-aidaptiv-ai-server\/","title":{"rendered":"DRACO 8\u00d7 RTX PRO 6000 aiDAPTIV+ AI Server"},"content":{"rendered":"<p><strong>768 GB of VRAM, eight GPUs, and still no liquid cooling.<\/strong> The densest air-cooled node RDP builds: eight NVIDIA RTX PRO 6000 Blackwell cards with a 16&nbsp;TB aiDAPTIV+ NVMe cache, delivering 180B+ capability into a standard rack without a CDU, a facility-water loop, or a datacenter rebuild.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>8\u00d7 NVIDIA RTX PRO 6000 Blackwell 96GB<\/strong> \u2014 <strong>768 GB aggregate VRAM<\/strong>.<\/li>\n<li><strong>16 TB aiDAPTIV+ cache<\/strong> (8\u00d7 2TB U.2) \u2014 several very large models resident simultaneously.<\/li>\n<li><strong>180B+ inference and fine-tuning<\/strong> at multi-tenant concurrency.<\/li>\n<li><strong>1 TB DDR5 ECC<\/strong> on a dual-Xeon host \u2014 the data pipeline keeps up with the GPUs.<\/li>\n<li><strong>Air-cooled 4U<\/strong> \u2014 the key practical advantage over SXM nodes that demand liquid.<\/li>\n<li><strong>2\u00d7 100GbE<\/strong> plus BMC\/Redfish out-of-band management.<\/li>\n<li>Deployable in an <strong>existing server room<\/strong>, on-premises, DPDP-aligned.<\/li>\n<\/ul>\n<h3>AI workload fit<\/h3>\n<ul>\n<li>High-concurrency <strong>inference<\/strong> for 180B+ models.<\/li>\n<li>Departmental and enterprise <strong>fine-tuning<\/strong> as a shared service.<\/li>\n<li>Large-scale production <strong>RAG<\/strong> with many simultaneous users.<\/li>\n<li><strong>Agentic AI<\/strong> back-ends running continuously.<\/li>\n<li><strong>Generative AI<\/strong>, <strong>computer vision<\/strong>, <strong>NLP &amp; speech<\/strong> at service scale.<\/li>\n<li><strong>Sovereign AI<\/strong> deployments where liquid cooling is not feasible on site.<\/li>\n<\/ul>\n<h3>AI workload positioning<\/h3>\n<p>The pragmatic ceiling for organisations whose facilities cannot take liquid cooling. You trade the interconnect bandwidth of an SXM\/NVLink node for eight PCIe cards that run on ordinary rack airflow \u2014 an excellent trade for inference and parameter-efficient fine-tuning, a poor one for large multi-GPU pretraining. We will tell you honestly which side of that line your workload sits on. 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, 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>Public sector &amp; sovereign:<\/strong> large-model capability in government facilities without water.<\/li>\n<li><strong>BFSI:<\/strong> high-concurrency document and risk inference inside the bank&#8217;s own DC.<\/li>\n<li><strong>Healthcare:<\/strong> hospital-group AI services with strict residency requirements.<\/li>\n<li><strong>Defence &amp; aerospace:<\/strong> air-gapped deployments in constrained facilities.<\/li>\n<li><strong>Enterprise &amp; GCCs:<\/strong> a shared AI platform node for multiple business units.<\/li>\n<\/ul>\n<h3>Performance &amp; how to be sure<\/h3>\n<p>Eight PCIe cards plus a 16 TB cache gives exceptional resident-model capacity; sustained multi-GPU training throughput will be below an equivalent NVLink\/SXM node, and we would rather say so up front than have it surface after purchase. 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>air-cooled<\/strong> server line. Below: <strong>QUASAR 4\u00d7 RTX PRO 6000<\/strong>. Above, where the facility supports it: <strong>H200 NVL<\/strong> and <strong>H200\/B200\/B300 SXM<\/strong> nodes with NVLink\/NVSwitch for true multi-GPU training, then rack-scale GB300 and AMD systems.<\/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, Kubernetes\/Slurm integration, and the aiDAPTIV+ memory-management layer &mdash; PyTorch-compliant, <strong>no model-code changes<\/strong>. Standard serving stacks (vLLM, SGLang, TensorRT-LLM) supported, plus the aiDAPTIVPro toolchain for ingest, RAG, fine-tune, monitor, validate and inference.<\/p>\n<h3>Power, thermal &amp; acoustics<\/h3>\n<p>4U with eight high-TDP GPUs and redundant PSUs \u2014 high per-rack power density on standard PDU feeds; a thermal survey of the intended rack is recommended before installation. <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 8 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 1TB DDR5 ECC \u00b7 16TB aiDAPTIV+ cache \u00b7 4U rack \u00b7 air-cooled<\/p>\n","protected":false},"featured_media":2026,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"DRACO 8\u00d7 RTX PRO 6000 aiDAPTIV+ AI Server \u2014 180B+ Air-Cooled | RDP GPU Mart","rank_math_description":"Eight RTX PRO 6000 Blackwell GPUs, 768GB VRAM and 16TB aiDAPTIV+ memory extension in an air-cooled 4U: 180B+ inference and fine-tuning on-premises. Request a quote from RDP.","_hermes_jsonld":""},"product_brand":[],"product_cat":[18],"product_tag":[],"class_list":["post-12619","product","type-product","status-publish","has-post-thumbnail","product_cat-gpu-servers","pa_form-factor-4u","pa_gpu-model-nvidia-rtx-pro-6000-blackwell","pa_industry-bfsi-hft","pa_industry-defence-aerospace","pa_industry-enterprise-gccs","pa_industry-healthcare-life-sciences","pa_industry-public-sector-sovereign-ai","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-dense-aidaptiv-training-inference","first","instock","taxable","shipping-taxable","product-type-external"],"_links":{"self":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product\/12619","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=12619"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media\/2026"}],"wp:attachment":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media?parent=12619"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_brand?post=12619"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_cat?post=12619"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_tag?post=12619"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}