{"id":115,"date":"2026-06-14T16:55:59","date_gmt":"2026-06-14T16:55:59","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/rdp-parallel-fs-storage-edge\/"},"modified":"2026-07-05T09:21:42","modified_gmt":"2026-07-05T09:21:42","slug":"quasar-50-tb-edge-ai-storage","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/quasar-50-tb-edge-ai-storage\/","title":{"rendered":"QUASAR 50 TB Edge AI Storage"},"content":{"rendered":"<p>The QUASAR 50 TB Edge AI Storage is an all-flash NVMe storage system built to keep GPUs fed during AI training and inference. It delivers 50 TB usable (NVMe) at 40 GB\/s read with GPUDirect Storage, so training data, checkpoints and model weights stream to the GPUs without the storage becoming the bottleneck \u2014 on-premises, in INR, on a GST invoice.<\/p>\n<p>Engineered for AI\/ML data pipelines at the edge, it pairs dense NVMe with a nfs\/s3 and high-speed networking, delivered racked, configured and validated as a single system with one warranty and one support contract.<\/p>\n<h3>Key highlights<\/h3>\n<ul>\n<li><strong>50 TB usable (NVMe) \u00b7 40 GB\/s read<\/strong> \u2014 high-bandwidth flash sized to feed GPU clusters during training.<\/li>\n<li><strong>GPUDirect Storage<\/strong> \u2014 data streams from NVMe straight to GPU memory, bypassing the CPU bounce for maximum throughput.<\/li>\n<li><strong>NFS\/S3 + GPUDirect Storage<\/strong> \u2014 parallel\/standard protocols so existing pipelines and frameworks just work.<\/li>\n<li><strong>12\u00d7 7.68 TB NVMe<\/strong> \u2014 dense, enterprise NVMe with end-to-end data integrity.<\/li>\n<li><strong>2\u00d7 100G<\/strong> \u2014 high-speed networking to the GPU fabric; no I\/O wall.<\/li>\n<li><strong>2.5M random read<\/strong> \u2014 high random-read IOPS for many small files and metadata-heavy AI datasets.<\/li>\n<li><strong>On-prem data sovereignty<\/strong> \u2014 datasets and weights stay in-house; DPDP-friendly, 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<\/ul>\n<h3>Where it fits<\/h3>\n<ul>\n<li><strong>Training data lake (primary):<\/strong> the high-bandwidth tier that streams datasets to GPU servers and clusters without starving them.<\/li>\n<li><strong>Checkpoints &amp; weights:<\/strong> fast write\/read of large checkpoints during long training runs.<\/li>\n<li><strong>RAG &amp; vector stores:<\/strong> low-latency storage for embeddings, indexes and retrieval corpora.<\/li>\n<li><strong>Inference assets:<\/strong> model weights and caches served at line rate to inference nodes.<\/li>\n<\/ul>\n<h3>How it works<\/h3>\n<p>An all-flash NVMe array exposes a NFS\/S3 + GPUDirect Storage namespace over 2\u00d7 100G. With GPUDirect Storage, reads bypass the CPU and land directly in GPU memory, so the GPUs spend time computing, not waiting on I\/O. The system scales by adding nodes; capacity and bandwidth grow together. <em>Honest note: real throughput depends on dataset shape, file sizes and the client fabric \u2014 we validate it on your data rather than quoting only a peak number.<\/em><\/p>\n<h3>Industry use cases<\/h3>\n<ul>\n<li><strong>AI\/ML platforms<\/strong> \u2014 feed GPU clusters with training data at full speed.<\/li>\n<li><strong>Government &amp; research<\/strong> \u2014 sovereign data lakes for national AI and HPC.<\/li>\n<li><strong>BFSI &amp; healthcare<\/strong> \u2014 high-throughput, data-resident storage under compliance.<\/li>\n<li><strong>Media &amp; design<\/strong> \u2014 fast scratch and asset storage for generative and rendering pipelines.<\/li>\n<li><strong>Manufacturing &amp; energy<\/strong> \u2014 sensor\/simulation datasets for AI and HPC.<\/li>\n<li><strong>Neocloud \/ AI providers<\/strong> \u2014 a high-bandwidth storage tier for a GPU cloud.<\/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: 50 TB usable (NVMe) at 40 GB\/s read and 2.5M random read is sized to keep GPU clusters fed. <strong>Want certainty? Request a free benchmark with your datasets and training loop on this exact configuration before you buy<\/strong>; we&#8217;ll send back real sustained throughput, IOPS and GPU-utilisation under your workload.<\/p>\n<h3>Series &amp; scale path<\/h3>\n<ul>\n<li><strong>QUASAR<\/strong> (performance storage tier) \u2014 <em>this<\/em>.<\/li>\n<li><strong>Capacity ladder:<\/strong> 50 TB edge \u2192 100\/250 TB \u2192 PB-scale parallel-FS \u2192 object storage at exabyte scale.<\/li>\n<li><strong>When to step up:<\/strong> grow capacity and bandwidth by adding nodes, or move to a PB-scale parallel filesystem \u2014 talk to an architect.<\/li>\n<\/ul>\n<h3>On-prem vs cloud \u2014 the TCO case<\/h3>\n<p>For AI data at scale, owning beats renting: cloud storage egress and per-GB costs add up fast against large, frequently-read datasets, and on-prem keeps data resident and the GPUs fed without network limits. RDP pricing is fixed in INR with a GST input-credit-eligible invoice \u2014 ask for a <strong>3-year TCO comparison<\/strong> including egress savings.<\/p>\n<h3>Software &amp; integration<\/h3>\n<p>Integrates with your stack: NFS\/S3 + GPUDirect Storage, NVIDIA GPUDirect Storage, and standard NFS\/S3 clients, with monitoring and quota\/multi-tenant management. Works with PyTorch\/TensorFlow data loaders, Slurm\/Kubernetes and common MLOps tooling.<\/p>\n<h3>Power, cooling &amp; rack integration<\/h3>\n<p>A 1U short-depth air-cooled system with redundant PSUs \u2014 specify rack power and cooling. <em>(Exact power draw, BTU, airflow and rack-depth figures confirmed on the build sheet.)<\/em> Full out-of-band management and drive hot-swap.<\/p>\n<h3>Deployment, warranty &amp; support<\/h3>\n<ul>\n<li><strong>Made to order<\/strong>, built, racked and burned-in in India; lead time confirmed at quote.<\/li>\n<li><strong>In the box:<\/strong> storage system, drives, rails, cabling, quick-start, and the configured filesystem.<\/li>\n<li><strong>Onsite warranty + AMC<\/strong> with pan-India coverage, drive-replacement SLA 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 an AI storage system, made to order \u2014 <strong>talk to an RDP solution architect<\/strong>, size capacity, bandwidth and fabric for your GPU cluster, get a 3-year TCO, and <strong>benchmark your own datasets before you commit.<\/strong> Request a quote to begin.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>50 TB usable (NVMe) \u00b7 40 GB\/s read \u00b7 NFS\/S3 + GPUDirect Storage \u00b7 2\u00d7 100G \u00b7 1U short-depth<\/p>\n","protected":false},"featured_media":2114,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"QUASAR 50 TB Edge AI Storage \u2014 40 GB\/s read, GPUDirect Storage | RDP GPU Mart","rank_math_description":"On-prem AI storage \u2014 50 TB usable (NVMe), 40 GB\/s read, NFS\/S3 + GPUDirect Storage, GPUDirect Storage. Keep your GPU cluster fed during training. Make-in-India, GST invoice, pan-India onsite. 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