DRACO 4× H200 NVL GPU Server

Overview

The DRACO 4× H200 NVL GPU Server is a Rack 4U rack server built to bring training and high-throughput inference into your own data centre. 4 NVIDIA H200 NVL GPUs deliver 564 GB HBM3e of high-bandwidth GPU memory in a dense, serviceable chassis — sized to train and serve 70B–180B-class models, behind your firewall, in INR, on a GST invoice.

Engineered for AI platform and MLOps teams standardising training and large-model serving on owned infrastructure, it pairs the GPUs with a 2× AMD EPYC 9005 host, 1 TB DDR5 ECC and 16 TB NVMe, with redundant power and full BMC/IPMI remote management — a production node that racks and runs, not a repurposed desktop.

Key highlights

• 564 GB HBM3e of GPU memory across 4× H200 NVL — train and serve 70B–180B-class models on-prem.
• NVLink-bridged GPUs — fast GPU-to-GPU transfer for efficient tensor parallelism, ECC throughout.
• 2× AMD EPYC 9005 + 1 TB DDR5 ECC — high core count and memory bandwidth to feed 4 data-centre GPUs.
• Rack 4U, redundant PSU, BMC/IPMI — hot-swap drives, tool-less service, lights-out management.
• 16 TB NVMe + 2× 25 GbE — fast dataset, checkpoint and weight storage with high-throughput networking; no egress fees.
• On-prem data sovereignty — 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 — grow within the DRACO server line and out to RDP rack-scale systems as demand rises.

AI workload fit (what it actually runs — honestly)

• Training & fine-tuning: full and parameter-efficient (QLoRA/LoRA) fine-tuning and training of 70B–180B-class models, with tensor- and data-parallelism across the GPUs.
• Inference: serve 70B–180B-class models at high throughput, or host several large models concurrently.
• RAG, vision, multimodal & agentic: production RAG endpoints, vision/multimodal inference and multi-agent back-ends on the 16 TB NVMe.
• Engineering note: the H200 NVL uses NVLink bridges between cards — much faster GPU-to-GPU than plain PCIe, though not the full all-to-all NVSwitch fabric of an SXM HGX node. For the densest tensor-parallel models, the SXM/HGX nodes are the step up.

AI workload positioning

This sits at the train-and-serve stage of the AI lifecycle. With 564 GB HBM3e of GPU memory, NVLink bridging, a 2× AMD EPYC 9005 host and fast NVMe, it is sized to sustain real training runs and production serving — where renting equivalent cloud GPUs around the clock becomes the dominant line in an AI budget.

Industry use cases

• BFSI — private model training and serving for fraud, risk and document intelligence under data-residency rules.
• Healthcare & life sciences — on-prem clinical NLP and imaging model training, PHI in-house.
• Government & PSU — sovereign AI on GeM-procurable infrastructure.
• SaaS / product — own your training and serving stack instead of renting.
• Telecom & manufacturing — AI close to operations.
• Research & higher-ed — a shared institutional training node.

Performance — and how to be sure

We don’t publish inflated peak numbers. The honest picture: 564 GB HBM3e of GPU memory across 4 GPUs is sized to train/fine-tune and serve 70B–180B-class models on-prem. Want certainty? Request a free benchmark of your model and dataset on this exact configuration before you buy — we’ll send back real tokens/sec and training/fine-tune timings for your workload.

Series & upgrade path

• DRACO (flagship training tier) — this.
• GPU-count ladder: 2-GPU → 4-GPU → 8-GPU within the line; step up to higher-memory SXM nodes (B200/B300) for the largest models.
• When to step up: for multi-rack scale, move to RDP Rack-Scale AI Systems and AI SuperClusters — talk to an architect about the fabric.

On-prem vs cloud — the TCO case

For sustained training and inference, owning beats renting: 4 continuously-running cloud GPUs of this class add up fast, and on-prem removes egress fees and keeps data and weights in-house. RDP pricing is fixed in INR with a GST input-credit-eligible invoice — ask for a 3-year TCO comparison against your current cloud spend.

Software & day-one readiness

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

Power, cooling & rack integration

A Rack 4U air-cooled node with redundant PSUs and substantial power draw — specify rack power and cooling capacity; liquid-cooling available on request. (Exact PSU rating, BTU, airflow and rack-depth figures confirmed on the build sheet.) BMC/IPMI provides remote power, console and health monitoring.

Deployment, warranty & support

• Made to order, built, racked-and-stacked and burned-in in India; realistic lead time confirmed at quote.
• In the box: server, 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 term & response window 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 training-and-serving server, made to order — talk to an RDP solution architect, get a configuration and 3-year TCO tailored to your workload, and benchmark your own model on it before you commit. Request a quote to begin.

Specifications