DRACO 256× H200 SXM AI SuperCluster

Overview

The DRACO 256× H200 SXM AI SuperCluster is a turnkey, liquid-cooled multi-rack AI supercluster — 256 NVIDIA H200 SXM GPUs across 32 HGX nodes in a 4-rack pod, wired with a non-blocking spine-leaf InfiniBand fabric. It delivers ~36 TB HBM3e of aggregate GPU memory and 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 the multi-vendor integration risk of building it yourself.

Key highlights

• 256× H200 SXM · ~36 TB HBM3e aggregate — data-hall-scale GPU memory for training and serving the largest models.
• Non-blocking spine-leaf InfiniBand (NDR/XDR) — full-bisection bandwidth for near-linear scaling across all 32 nodes.
• NVLink + NVSwitch within each node — full intra-node bandwidth, complemented by the InfiniBand spine between nodes.
• 64× Intel Xeon 6 (32 nodes) + 64 TB DDR5 ECC — host compute and memory matched to 256 data-centre GPUs.
• 4 PB parallel NVMe parallel filesystem — high-throughput training data and checkpoint storage at cluster scale.
• 4-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.

AI workload fit (what it actually runs — honestly)

• Frontier training: distributed pre-training of large foundation models across the 256 GPUs with 3D parallelism (data, tensor, pipeline).
• 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: production AI platforms for an entire organisation on the cluster’s storage and fabric.
• Engineering note: at supercluster scale the limiting factor is the network, not a single GPU — this system uses a non-blocking spine-leaf InfiniBand fabric so collective (all-reduce) traffic scales. Real efficiency depends on model and parallelism strategy; we run a scaling test on your workload rather than quoting a peak FLOPS number.

AI workload positioning

This sits at the data-hall / foundation-model stage: a complete AI supercomputer rather than a server or single rack. With ~36 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, where the meter never stops.

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 and platform development at scale.

Performance — and how to be sure

We don’t publish inflated peak numbers. The honest picture: 256 H200 SXM GPUs (~36 TB HBM3e) on a non-blocking InfiniBand 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: 256-GPU pod → 512-GPU pod → GB200/GB300 NVL72 SuperPODs (hundreds–thousands of GPUs).
• When to step up: for unified NVLink domains at rack scale, see RDP NVL72 Rack-Scale systems; for the largest SuperPODs, scale this fabric out — talk to an architect about the data hall.

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 job scheduler. Optional managed cluster operations and an MLOps platform.

Power, cooling & rack integration

A 4-rack pod liquid-cooled supercluster with high power density — RDP scopes facility power, CDU/manifold and water, the InfiniBand spine, floor layout and redundancy as part of the reference-architecture design. (Exact power, BTU, flow and facility figures confirmed in the design package.) Full out-of-band management across the cluster.

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