{"id":12696,"date":"2026-08-18T22:16:28","date_gmt":"2026-08-18T22:16:28","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/draco-72x-mi455x-rack-scale-ai-system\/"},"modified":"2026-08-18T23:24:42","modified_gmt":"2026-08-18T23:24:42","slug":"draco-72x-mi455x-rack-scale-ai-system","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/draco-72x-mi455x-rack-scale-ai-system\/","title":{"rendered":"DRACO 72\u00d7 MI455X Rack-Scale AI System"},"content":{"rendered":"

Rack-scale AI without the interconnect lock-in.<\/strong> Seventy-two AMD Instinct MI455X accelerators in a single liquid-cooled rack, joined by an all-Ethernet, open-standards fabric<\/strong> rather than a proprietary one. Built for organisations that want frontier-class capacity and a second source of silicon — and who would rather their next decade of AI infrastructure not depend on one vendor’s roadmap.<\/p>\n

Key highlights<\/h3>\n
    \n
  • 72\u00d7 AMD Instinct MI455X<\/strong> (CDNA 5, 2 nm) \u2014 AMD’s frontier-class accelerator.<\/li>\n
  • ~31 TB unified HBM4<\/strong> per rack \u2014 a memory-capacity advantage AMD leads on.<\/li>\n
  • 432 GB HBM4 per GPU<\/strong> \u2014 materially more per-accelerator memory than the Blackwell generation.<\/li>\n
  • All-Ethernet open fabric<\/strong> \u2014 no proprietary scale-up interconnect; standard optics and switching.<\/li>\n
  • ~2.9 exaFLOPS FP4<\/strong> per rack (AMD-published) for low-precision inference and training.<\/li>\n
  • Liquid-cooled<\/strong> rack-scale integration, delivered and commissioned by RDP.<\/li>\n
  • Sovereign-capable<\/strong>: deployable on-soil, air-gap possible, DPDP-aligned.<\/li>\n<\/ul>\n

    AI workload fit<\/h3>\n
      \n
    • Frontier LLM training<\/strong> at trillion-parameter scale.<\/li>\n
    • Full-parameter fine-tuning<\/strong> of very large models.<\/li>\n
    • High-throughput inference<\/strong> at FP4\/FP8 for national or multi-tenant services.<\/li>\n
    • HPC & AI<\/strong> convergence \u2014 AMD’s traditional strength in FP64 science.<\/li>\n
    • Sovereign AI<\/strong> programmes seeking supply-chain diversity.<\/li>\n
    • The compute core of an AI factory<\/strong> built on open standards.<\/li>\n<\/ul>\n

      AI workload positioning<\/h3>\n

      This is the credible open-standards answer to proprietary rack-scale AI. The strategic argument is memory capacity and fabric openness<\/strong>: more HBM per accelerator, and Ethernet rather than a closed scale-up fabric, which keeps switching, optics and future upgrades in a competitive market. The honest counter-argument is software maturity<\/strong> \u2014 CUDA has a deeper ecosystem than ROCm, and any migration carries real engineering cost. RDP will quantify that cost for your specific stack rather than wave it away. All AMD performance and capacity figures quoted here are AMD-published<\/strong> and are presented as such — they are not RDP measurements. Availability follows AMD’s stated schedule and allocation; RDP confirms silicon availability, delivery window and final configuration in writing at quotation.<\/p>\n

      Industry use cases<\/h3>\n
        \n
      • Public sector & sovereign:<\/strong> national AI capability with deliberate supply-chain diversification.<\/li>\n
      • Neocloud:<\/strong> differentiated capacity and better memory-per-GPU economics for tenants.<\/li>\n
      • Research & education:<\/strong> HPC + AI convergence where FP64 matters alongside AI.<\/li>\n
      • Defence & aerospace:<\/strong> strategic independence from a single accelerator vendor.<\/li>\n
      • Enterprise & GCCs:<\/strong> hedging a multi-year AI infrastructure commitment.<\/li>\n<\/ul>\n

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

        AMD’s headline for this generation is memory: 432 GB HBM4 per GPU and ~31 TB per rack, which is what lets very large models stay resident without aggressive sharding. 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

        Below: RDP’s 8\u00d7 SXM<\/strong> single nodes (NVIDIA) and AMD Instinct servers. Beside: the NVIDIA GB300 NVL72<\/strong> rack-scale system \u2014 the direct comparison, and RDP quotes both honestly. Above: multi-rack AMD and NVIDIA superclusters and the GB300 NVL72 Supercluster<\/strong> containerised node.<\/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

        Software stack is ROCm<\/strong>-based: ROCm runtime and libraries, PyTorch and JAX support, vLLM and SGLang for serving, Kubernetes\/Slurm orchestration, plus cluster management and telemetry. Migration from CUDA is a real engineering exercise and RDP scopes it honestly as part of the proposal rather than treating it as a drop-in swap.<\/p>\n

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

        Full liquid-cooled rack with in-row CDU and high-capacity busway feeds. Facility power, cooling capacity, floor loading and water-loop readiness are surveyed before order \u2014 this is a datacenter project, not a delivery. 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 (HSN 8471), pan-India onsite support, and availability through GeM for public-sector procurement. Built to order \u2014 allocation-dependent; AMD MI400-series systems ship from 2H-2026. 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":"

        72\u00d7 AMD Instinct MI455X \u00b7 ~31TB HBM4 \u00b7 all-Ethernet fabric \u00b7 liquid-cooled rack<\/p>\n","protected":false},"featured_media":2001,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"DRACO 72\u00d7 AMD MI455X Rack-Scale AI System \u2014 Open-Standard NVL72 Alternative | RDP","rank_math_description":"RDP-branded 72-GPU AMD Instinct MI455X rack: ~31TB HBM4, CDNA 5, all-Ethernet open fabric, liquid-cooled. The open-standards alternative to NVL72 rack-scale AI. Request a quote.","_hermes_jsonld":""},"product_brand":[],"product_cat":[17],"product_tag":[],"class_list":["post-12696","product","type-product","status-publish","has-post-thumbnail","product_cat-rack-scale-ai-systems","pa_form-factor-rack","pa_gpu-model-amd-instinct-mi455x","pa_industry-defence-aerospace","pa_industry-enterprise-gccs","pa_industry-neocloud","pa_industry-public-sector-sovereign-ai","pa_industry-research-higher-education","pa_series-draco","pa_use-case-agentic-ai","pa_use-case-fine-tuning","pa_use-case-generative-ai","pa_use-case-hpc-ai","pa_use-case-inference","pa_use-case-llm-training","pa_use-case-sovereign-ai","pa_workload-fit-amd-rack-scale-frontier-training","first","instock","taxable","shipping-taxable","product-type-external"],"_links":{"self":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product\/12696","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=12696"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media\/2001"}],"wp:attachment":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media?parent=12696"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_brand?post=12696"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_cat?post=12696"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_tag?post=12696"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}