DRACO 64× H200 Multi-Node System

Overview

The DRACO 64× H200 Multi-Node System is a dense 16-blade chassis system that packs 64 NVIDIA H200 SXM5 GPUs (9,024 GB HBM3e) into one shared-infrastructure chassis with pooled power and liquid cooling. It consolidates large-scale training and high-throughput inference into one efficient, serviceable footprint on-premises, in INR, on a GST invoice.

Engineered for platform teams who want training-grade density, it shares power, cooling and management across the blades, with NVLink inside each blade and a non-blocking InfiniBand fabric between them — delivered racked, cabled and validated as a single SKU with one warranty.

Key highlights

• 64× H200 · 9,024 GB HBM3e — training-grade GPU density for distributed training and high-throughput inference.
• NVLink in-blade + non-blocking InfiniBand — efficient in-blade tensor parallelism and low-latency collectives across the system.
• 32× Intel Xeon 6 (16 blades) + 16 TB DDR5 ECC — host compute and memory matched to 64 data-centre GPUs.
• 320 TB NVMe NVMe + InfiniBand NDR 400G — fast local storage and a high-bandwidth fabric for multi-blade jobs.
• Liquid-cooled, hot-swap, serviceable — blade-level service, redundant shared PSUs, full BMC/IPMI.
• 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 — add blades, or step up to rack-scale systems and SuperClusters.

AI workload fit (what it actually runs — honestly)

• Distributed training (primary): train and fine-tune large models across the NVLink-connected blades, scaling over the InfiniBand fabric.
• Large-model serving: shard the largest models across blades, or host many large models concurrently at density.
• RAG, vision, multimodal & agentic: dense production AI services and multi-agent back-ends.
• Engineering note: each blade’s H200 GPUs are NVLink-connected for efficient in-blade tensor parallelism, and blades scale over a non-blocking InfiniBand fabric — this system trains and serves large models at density. The strength is training-grade GPU density in one serviceable chassis.

AI workload positioning

This sits at the train-and-serve at density stage: a density play that puts many training-grade GPUs in one serviceable chassis. With 9,024 GB HBM3e across 64 GPUs, it is sized to sustain distributed training and large-scale serving in one footprint — more efficient than many separate servers, simpler than a full rack-scale build.

Industry use cases

• Neocloud / AI providers — dense, multi-tenant training-grade GPU capacity per rack.
• BFSI — a consolidated private training and inference fleet under data-residency rules.
• SaaS / product — train and host many models in one managed system.
• Healthcare & research — shared departmental training density, data in-house.
• Manufacturing & telecom — consolidated large-model development.
• Government & PSU — efficient sovereign GPU capacity on GeM.

Performance — and how to be sure

We don’t publish inflated peak numbers. The honest picture: 9,024 GB HBM3e across 64 GPUs in one shared chassis is sized for distributed training and high-throughput serving. Want certainty? Request a free benchmark of your model and dataset, including a scaling test, on this exact configuration before you buy; we’ll send back real tokens/sec, scaling efficiency and density figures.

Series & upgrade path

• DRACO (flagship density tier) — this.
• Density ladder: twin → compute-block → blade-chassis; choose H200 or B200 SXM for training density.
• When to step up: for the largest tightly-coupled training, move to NVL-class rack-scale systems — talk to an architect.

On-prem vs cloud — the TCO case

For sustained training-grade density, owning beats renting: a continuously-running cloud GPU fleet of this size dominates an AI budget, and on-prem removes egress and keeps data and weights in-house. Shared power/cooling lowers per-GPU operating cost. RDP pricing is fixed in INR with a GST input-credit-eligible invoice — ask for a 3-year TCO comparison.

Software & day-one readiness

Ships pre-configured to train and serve: NVIDIA driver, CUDA, cuDNN, NCCL, the InfiniBand stack, Docker and NVIDIA Container Toolkit, with Kubernetes/Slurm, PyTorch and vLLM / Triton / TensorRT-LLM on Ubuntu LTS. Optional managed operations and observability.

Power, cooling & rack integration

A Dual Blade Chassis 16U liquid-cooled system with shared, redundant PSUs — plan rack power, cooling and the InfiniBand fabric. (Exact PSU rating, BTU, flow and rack-depth figures confirmed on the build sheet.) Single management plane via BMC/IPMI across the blades.

Deployment, warranty & support

• Made to order, built, racked, cabled and burned-in in India; lead time confirmed at quote.
• In the box: chassis/blades, rails, power cables, quick-start, and the pre-installed AI software stack.
• Onsite warranty + AMC with pan-India coverage 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 dense, training-grade multi-node GPU system, made to order — talk to an RDP solution architect, size the density, fabric and cooling for your workload, get a 3-year TCO, and benchmark your own model before you commit. Request a quote to begin.

Specifications