DRACO 512× B200 SXM AI SuperCluster

Overview

The DRACO 512× B200 SXM AI SuperCluster is a turnkey, liquid-cooled multi-rack AI supercluster delivering ~92 TB HBM3e of aggregate GPU memory across 64 HGX B200 nodes. It arrives as a complete, validated system — power, cooling, fabric, storage and software — ready to train frontier models on-premises, in INR, on a GST invoice.

Engineered for sovereign-AI programmes, neoclouds and large enterprises building data-hall-scale capacity, it is delivered as a single engagement: RDP designs the reference architecture, integrates and burns it in, and hands over one validated supercluster with one warranty and one support contract — removing multi-vendor integration risk.

Key highlights

• 512× B200 SXM · ~92 TB HBM3e aggregate — data-hall-scale GPU memory for training and serving the largest models.
• NVLink/NVSwitch per node + non-blocking InfiniBand spine — NVLink + NVSwitch within each HGX node, joined by a non-blocking spine-leaf InfiniBand fabric across the 64 nodes.
• 128× Intel Xeon 6 (64 nodes) + 128 TB DDR5 ECC — host/CPU compute matched to 512 Blackwell GPUs.
• 8 PB parallel NVMe parallel filesystem — high-throughput training data and checkpoint storage at cluster scale.
• 8-Rack pod, liquid-cooled, turnkey — delivered racked, cabled, cooled, validated; one SKU, one warranty.
• On-prem data sovereignty — training data and weights stay in-house; DPDP-friendly, air-gappable.
• Make-in-India OEM — predictable INR pricing, GST tax invoice (HSN 8471), pan-India onsite support, GeM-procurable.
• Scale path — extend the fabric to larger SuperPODs and multi-pod data halls.

AI workload fit (what it actually runs — honestly)

• Frontier training: distributed pre-training of large foundation models across the 512 GPUs with 3D parallelism.
• Large-scale fine-tuning & serving: fine-tune and serve many large models in parallel, or shard the very largest across nodes.
• RAG, multimodal & agentic platforms: organisation-wide production AI on the cluster’s storage and fabric.
• Engineering note: at supercluster scale the network is the limiter, not a single GPU — a non-blocking spine-leaf InfiniBand fabric keeps collective (all-reduce) traffic scaling across 512 GPUs. Real efficiency depends on model and parallelism strategy; we run a scaling test on your workload.

AI workload positioning

This sits at the data-hall / foundation-model stage: a complete AI supercomputer. With ~92 TB HBM3e of GPU memory on a non-blocking fabric, it is sized to sustain frontier-model training and organisation-wide serving on-prem — the sovereign, owned alternative to renting a hyperscale cloud cluster.

Industry use cases

• Government & sovereign AI — a national foundation-model supercluster on GeM-procurable infrastructure.
• Neocloud / AI providers — a competitive GPU-cloud region built and validated end-to-end.
• BFSI & conglomerates — private large-model training under data-residency rules.
• Healthcare & life sciences — large-scale research and imaging programmes, data in-house.
• Research & national labs — an institutional AI supercomputer.
• Telecom — in-house foundation-model development at scale.

Performance — and how to be sure

We don’t publish inflated peak numbers. The honest picture: 512 Blackwell GPUs (~92 TB HBM3e) on a non-blocking fabric are sized for frontier-scale distributed training and organisation-wide serving. Want certainty? Request a free scaling benchmark of your model and dataset on a representative configuration before you commit; we’ll send back real tokens/sec, scaling efficiency and projected time-to-train.

Series & upgrade path

• DRACO (flagship supercluster tier) — this.
• Scale ladder: SXM pods (256–512 GPU) and GB200/GB300 NVL72 SuperPODs (hundreds–thousands of GPUs).
• When to step up: extend to larger GB300 SuperPODs or multi-pod data halls — talk to an architect about the fabric and facility.

On-prem vs cloud — the TCO case

At supercluster scale, owning is a strategic and economic decision: an always-on hyperscale cloud cluster dominates any AI budget over a multi-year horizon, while on-prem removes egress and keeps sovereign data and weights in-house. RDP pricing is fixed in INR with a GST input-credit-eligible invoice — ask for a multi-year TCO and financing model.

Software & day-one readiness

Ships pre-integrated and validated: NVIDIA driver, CUDA, cuDNN, NCCL, the InfiniBand stack, Slurm and/or Kubernetes, container registry, PyTorch and vLLM / Triton / TensorRT-LLM on Ubuntu LTS, with monitoring and a scheduler. Optional managed cluster operations and an MLOps platform.

Power, cooling & rack integration

A 8-rack pod liquid-cooled supercluster with high power density — RDP scopes facility power, CDU/manifold and water, the InfiniBand spine, floor layout and redundancy in the reference-architecture design. (Exact power, BTU, flow and facility figures confirmed in the design package.) Full out-of-band management.

Deployment, warranty & support

• Made to order, integrated, racked, cabled, cooled and burned-in in India; project timeline confirmed at quote.
• Delivered as one system: nodes, leaf/spine switches, cabling, PDUs, cooling, parallel storage, and the full cluster software stack.
• Onsite warranty + AMC with pan-India coverage, cluster-level SLAs and an RMA/escalation path (exact terms confirmed at quote).

Why RDP

14 years of Make-in-India infrastructure and 300,000+ devices shipped. Indian OEM, INR pricing, GST tax invoice (HSN 8471), pan-India onsite engineers, GeM availability, and DPDP / sovereign-AI-ready deployment.

Buy with confidence

This is a turnkey AI supercluster, made to order — talk to an RDP solution architect, co-design the reference architecture, fabric and data hall, get a multi-year TCO and financing plan, and run a scaling benchmark before you commit. Request a quote to begin.

Specifications