{"id":15288,"date":"2026-08-28T01:30:54","date_gmt":"2026-08-28T01:30:54","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/quasar-ryzen-ai-max-ai-nas-appliance\/"},"modified":"2026-08-30T15:14:00","modified_gmt":"2026-08-30T15:14:00","slug":"quasar-ryzen-ai-max-ai-nas-appliance","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/quasar-ryzen-ai-max-ai-nas-appliance\/","title":{"rendered":"QUASAR Ryzen AI Max AI NAS Appliance"},"content":{"rendered":"

Move the model to the data, not the data to the model.<\/strong> A five-bay storage appliance with the full Ryzen AI Max platform inside — up to 128 GB unified memory<\/strong>, a 50 TOPS NPU and 10 Gigabit Ethernet<\/strong> — so indexing, embedding and retrieval run on the box that holds the files. For RAG over a document estate, that removes the copy step entirely.<\/p>\n

Key highlights<\/h3>\n
    \n
  • 5 \u00d7 3.5\u2033 drive bays<\/strong> with a magnetic front panel for tool-free service.<\/li>\n
  • 10GbE RJ45<\/strong> \u2014 the network is not the bottleneck for model or dataset loads.<\/li>\n
  • Up to 128 GB unified memory<\/strong> and a 50 TOPS NPU inside the storage appliance<\/em>.<\/li>\n
  • Compute where the data lives<\/strong> \u2014 index and embed without copying to a workstation.<\/li>\n
  • 200 W internal TFX PSU<\/strong>, 120 W platform envelope, aluminium-alloy chassis.<\/li>\n
  • 222.5 \u00d7 222.5 \u00d7 224 mm<\/strong> \u2014 desk-side or shelf, not a rack requirement.<\/li>\n
  • DP 1.4 output and USB for direct local administration.<\/li>\n<\/ul>\n

    AI workload fit<\/h3>\n
      \n
    • RAG<\/strong> over a document estate held on the same appliance.<\/li>\n
    • Local inference<\/strong> against files without a copy step.<\/li>\n
    • NLP & speech<\/strong> batch processing of stored recordings and documents.<\/li>\n
    • Computer vision<\/strong> indexing of an image or video archive.<\/li>\n
    • Generative AI<\/strong> asset workflows for small studios and teams.<\/li>\n
    • Departmental file services with AI enrichment built in.<\/li>\n<\/ul>\n

      AI workload positioning<\/h3>\n

      A conventional NAS stores; this one understands what it stores<\/strong>. Putting a 128 GB unified-memory AI platform behind the drives collapses the usual RAG pipeline \u2014 no staging copy to a GPU box, no second machine to secure, no data leaving the appliance. Be clear on the boundary: this is an inference and indexing<\/strong> appliance, and drive capacity, RAID level and filesystem are specified per deployment rather than fixed here.<\/p>\n

      Industry use cases<\/h3>\n
        \n
      • Healthcare:<\/strong> clinical document and imaging archives with on-appliance retrieval under DPDP.<\/li>\n
      • BFSI:<\/strong> regulated document stores with local search and summarisation.<\/li>\n
      • Enterprise & GCCs:<\/strong> departmental knowledge bases that stay inside the building.<\/li>\n
      • Public sector & sovereign:<\/strong> records systems with on-soil AI enrichment.<\/li>\n
      • Research & education:<\/strong> dataset stores with indexing and embedding at rest.<\/li>\n<\/ul>\n

        Performance & how to be sure<\/h3>\n

        The meaningful measurements here are 10GbE line rate, drive throughput and embedding rate over your own corpus<\/strong> \u2014 not a synthetic token figure, since the workload is retrieval-shaped rather than generation-shaped. Rather than quote a tokens\/sec figure that will not match your workload, RDP offers a “benchmark your model”<\/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

        Series & upgrade path<\/h3>\n

        QUASAR<\/strong> performance tier within Storage Systems. Beside: RDP all-flash NVMe<\/strong> arrays when raw throughput matters more than on-box AI. Above: parallel-filesystem<\/strong> storage for GPU-server and cluster feeds. Pairs naturally with any CARINA\/QUASAR desk-side unit that queries it.<\/p>\n

        On-prem vs cloud (TCO)<\/h3>\n

        For sustained daily AI work this configuration removes per-GPU-hour billing, queueing for scarce instances, and egress charges on your own data — 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

        Software & day-one readiness<\/h3>\n

        Ships with Ubuntu or Windows plus the AMD ROCm<\/strong> stack, and is commissioned with a retrieval pipeline of your choice \u2014 vector store, embedding model and file-watcher \u2014 configured against your actual document set rather than a demo corpus. Drive configuration, RAID level and filesystem are specified with you at quotation.<\/p>\n

        Power, thermal & acoustics<\/h3>\n

        200 W internal TFX PSU, 120 W platform envelope, standard 230 V socket. Aluminium-alloy chassis; air-cooled and quiet enough for an office shelf rather than a server room. Exact wattage, BTU and dB(A) figures come from RDP bench measurement rather than estimates — ask for the site-readiness sheet with your quote.<\/em><\/p>\n

        Deployment, warranty & support<\/h3>\n

        Built to order by RDP Technologies. Supplied with GST invoice, pan-India onsite support, and availability through GeM for public-sector procurement. Built to order \u2014 typically 5\u20137 weeks; drive configuration confirmed at quotation.<\/p>\n

        Why RDP<\/h3>\n

        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 — with predictable INR pricing, GST invoicing and pan-India onsite service.<\/p>\n

        Buy with confidence<\/h3>\n

        Use 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":"

        Ryzen AI Max \u00b7 5-bay \u00b7 10GbE \u00b7 up to 128GB unified \u00b7 AI-native NAS<\/p>\n","protected":false},"featured_media":1950,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"QUASAR Ryzen AI Max 5-Bay AI NAS Appliance | RDP GPU Mart","rank_math_description":"AI-native 5-bay NAS on AMD Ryzen AI Max: 10GbE, up to 128 GB unified memory, 50 TOPS NPU. Inference where the data lives. Request a quote from RDP.","_hermes_jsonld":""},"product_brand":[],"product_cat":[25],"product_tag":[],"class_list":["post-15288","product","type-product","status-publish","has-post-thumbnail","product_cat-storage-systems","pa_form-factor-desktop","pa_gpu-model-amd-radeon-8060s","pa_industry-bfsi-hft","pa_industry-enterprise-gccs","pa_industry-healthcare-life-sciences","pa_industry-public-sector-sovereign-ai","pa_industry-research-higher-education","pa_series-quasar","pa_use-case-computer-vision","pa_use-case-generative-ai","pa_use-case-inference","pa_use-case-nlp-speech","pa_use-case-rag","pa_workload-fit-ai-native-network-storage","first","instock","taxable","shipping-taxable","product-type-external"],"_links":{"self":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product\/15288","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=15288"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media\/1950"}],"wp:attachment":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media?parent=15288"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_brand?post=15288"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_cat?post=15288"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_tag?post=15288"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}