{"id":99,"date":"2026-06-14T16:55:59","date_gmt":"2026-06-14T16:55:59","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/rdp-agentic-ai-pc-pro-max\/"},"modified":"2026-07-06T01:47:37","modified_gmt":"2026-07-06T01:47:37","slug":"quasar-2x-rtx-pro-5000-blackwell-agentic-ai-pc","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/quasar-2x-rtx-pro-5000-blackwell-agentic-ai-pc\/","title":{"rendered":"QUASAR 2\u00d7 RTX PRO 5000 Blackwell Agentic AI PC"},"content":{"rendered":"<p>The QUASAR 2\u00d7 RTX PRO 5000 Blackwell Agentic AI PC runs many AI agents locally \u2014 a tower workstation with 2\u00d7 NVIDIA RTX PRO 5000 Blackwell (96 GB GDDR7) built to run private LLM agents, RAG and automation at scale on your own hardware, offline if needed. It puts serious agentic AI on the desk without sending prompts or data to the cloud \u2014 in INR, on a GST invoice.<\/p>\n<p>Engineered for teams running multiple concurrent agents or larger local models, it pairs a high-core Xeon W with two Blackwell GPUs, plus large memory and fast NVMe, so local models and agent fleets respond instantly and keep data in-house.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>2\u00d7 RTX PRO 5000 Blackwell \u00b7 96 GB GDDR7<\/strong> \u2014 run larger local LLMs (up to 70B) and many concurrent agents on-device.<\/li>\n<li><strong>Dual Blackwell GPUs + Xeon W<\/strong> \u2014 data-parallel serving of multiple models or larger quantised models for agent fleets.<\/li>\n<li><strong>Intel Xeon W-3500 + 256 GB DDR5 ECC<\/strong> \u2014 high-core orchestration for many multi-step agents and RAG.<\/li>\n<li><strong>8 TB NVMe NVMe<\/strong> \u2014 large local model store, vector DB and document corpus; no egress.<\/li>\n<li><strong>Tower, 10 GbE<\/strong> \u2014 quiet desk-side footprint with high-throughput networking.<\/li>\n<li><strong>Private &amp; offline-capable<\/strong> \u2014 prompts, data and models stay on-device; air-gappable.<\/li>\n<li><strong>Make-in-India OEM<\/strong> \u2014 predictable INR pricing, GST tax invoice (HSN 8471), pan-India onsite support, GeM-procurable.<\/li>\n<li><strong>Upgrade path<\/strong> \u2014 step up to an agentic inference server for fleet-scale concurrent agents, or an AI Workstation for heavy fine-tuning.<\/li>\n<\/ul>\n<h3>AI workload fit (what it actually runs \u2014 honestly)<\/h3>\n<ul>\n<li><strong>Local agents at scale (primary):<\/strong> run up to 70B models and many concurrent agentic workflows \u2014 tool use, RAG, automation and copilots.<\/li>\n<li><strong>RAG &amp; document AI:<\/strong> on-device retrieval over large local corpora and vector DBs.<\/li>\n<li><strong>Fine-tuning, vision &amp; speech:<\/strong> QLoRA fine-tuning, local CV, speech and multimodal inference.<\/li>\n<li><em>Engineering note:<\/em> with 96 GB GDDR7 of GPU memory this serves up to 70B quantised models; the two PCIe GPUs (no NVLink) are ideal for data-parallel multi-model serving \u2014 for one model larger than 96 GB use tensor parallelism across the pair. It is built for responsive local agent fleets, not large-scale training.<\/li>\n<\/ul>\n<h3>AI workload positioning<\/h3>\n<p>This sits at the <strong>local-agent-fleet<\/strong> stage: the machine that runs many AI agents privately, on the desk. With 96 GB GDDR7 across 2 Blackwell GPUs and a high-core Xeon W, it is sized to <strong>sustain<\/strong> concurrent local inference and agent loops \u2014 where cloud APIs are slow, costly or non-compliant.<\/p>\n<h3>Industry use cases<\/h3>\n<ul>\n<li><strong>Software &amp; product teams<\/strong> \u2014 private coding copilots and multi-agent development.<\/li>\n<li><strong>BFSI &amp; legal<\/strong> \u2014 confidential document AI and agent fleets under data-residency rules.<\/li>\n<li><strong>Healthcare<\/strong> \u2014 on-device clinical assistants keeping PHI local.<\/li>\n<li><strong>Design &amp; media<\/strong> \u2014 local creative and generative AI at scale.<\/li>\n<li><strong>Government &amp; defence<\/strong> \u2014 air-gapped private agent fleets.<\/li>\n<li><strong>Research<\/strong> \u2014 a shared local-AI workstation for a team.<\/li>\n<\/ul>\n<h3>Performance \u2014 and how to be sure<\/h3>\n<p>We don&#8217;t publish inflated peak numbers. The honest picture: 96 GB GDDR7 across 2 GPUs is sized to run up to 70B models and many concurrent agents locally. <strong>Want certainty? Request a free benchmark of your agents and models on this exact configuration before you buy<\/strong>; we&#8217;ll send back real tokens\/sec, concurrency and latency.<\/p>\n<h3>Series &amp; upgrade path<\/h3>\n<ul>\n<li><strong>QUASAR<\/strong> (performance local-AI tier) \u2014 <em>this<\/em>.<\/li>\n<li><strong>Ladder:<\/strong> mini\/edge \u2192 agentic AI PC \u2192 multi-GPU agentic PC \u2192 agentic inference server.<\/li>\n<li><strong>When to step up:<\/strong> for fleet-scale concurrency, move to an agentic inference server; for heavy fine-tuning, an AI Workstation \u2014 talk to an architect.<\/li>\n<\/ul>\n<h3>On-prem vs cloud \u2014 the TCO case<\/h3>\n<p>Running an agent fleet locally removes per-token API costs and keeps data on-device: for steady agentic use, a one-time machine beats a recurring cloud bill, with full privacy. RDP pricing is fixed in INR with a GST input-credit-eligible invoice \u2014 ask for a <strong>cost-per-agent comparison<\/strong> vs cloud APIs.<\/p>\n<h3>Software &amp; day-one readiness<\/h3>\n<p>Ships <strong>ready to run agents<\/strong>: NVIDIA driver, CUDA, cuDNN, Docker, with Ollama \/ vLLM \/ Triton, a local vector DB and popular agent frameworks pre-configured on Ubuntu or Windows. Optional managed local-AI stack and model library.<\/p>\n<h3>Power, cooling &amp; rack integration<\/h3>\n<p>A tower workstation with quiet desk-side operation \u2014 specify a dedicated circuit for the dual-GPU configuration. <em>(Exact power draw, thermal and acoustic figures confirmed on the build sheet.)<\/em><\/p>\n<h3>Deployment, warranty &amp; support<\/h3>\n<ul>\n<li><strong>Made to order<\/strong>, built and burned-in in India; lead time confirmed at quote.<\/li>\n<li><strong>In the box:<\/strong> system, power supply, quick-start, and the pre-installed local-AI software stack.<\/li>\n<li><strong>Onsite warranty + AMC<\/strong> with pan-India coverage and an RMA\/escalation path <em>(exact terms confirmed at quote)<\/em>.<\/li>\n<\/ul>\n<h3>Why RDP<\/h3>\n<p>14 years of Make-in-India infrastructure and <strong>300,000+ devices shipped<\/strong>. Indian OEM, INR pricing, GST tax invoice (HSN 8471), pan-India onsite engineers, GeM availability, and DPDP \/ sovereign-AI-ready deployment.<\/p>\n<h3>Buy with confidence<\/h3>\n<p>This is a local-AI machine for running agent fleets privately, made to order \u2014 <strong>talk to an RDP solution architect<\/strong>, size it for your agents and models, and <strong>benchmark your own workload on it before you commit.<\/strong> Request a quote to begin.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Intel Xeon W-3500 \u00b7 256 GB DDR5 ECC \u00b7 8 TB NVMe \u00b7 96 GB GDDR7 \u00b7 Tower<\/p>\n","protected":false},"featured_media":1983,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"QUASAR 2\u00d7 RTX PRO 5000 Blackwell Agentic AI PC \u2014 96 GB GDDR7, local agent fleet | RDP GPU Mart","rank_math_description":"Agentic AI PC for private local AI \u2014 2\u00d7 RTX PRO 5000 Blackwell (96 GB GDDR7), Xeon W-3500. Run up to 70B LLMs and many agents on-device, offline. Make-in-India, GST invoice. 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