DRACO 4× H200 NVL AI Server

Overview

NVLink bandwidth in a rack you already have. Four NVIDIA H200 NVL cards — 564 GB of HBM3e — bridged 4-way by NVLink in an air-cooled 4U chassis. The practical way to get true multi-GPU interconnect without an SXM baseboard, a CDU, or a facility water loop.

Key highlights

• 4× NVIDIA H200 NVL 141GB HBM3e — 564 GB of high-bandwidth memory.
• 4-way NVLink bridge — GPU-to-GPU bandwidth far beyond PCIe peer-to-peer.
• Air-cooled 4U, standard rack, standard PDU — installable in an existing server room.
• 512 GB DDR5 ECC on a dual-Xeon host with 2× 100GbE and BMC/Redfish.
• Runs the same CUDA stack as SXM nodes — no software migration when you scale.
• PCIe serviceability: cards are field-replaceable individually, unlike an SXM baseboard.
• On-premises and DPDP-aligned; suitable for air-gapped deployment.

AI workload fit

• Multi-GPU training and full fine-tuning at 70B–180B.
• Long-context inference where HBM capacity is the binding constraint.
• Production RAG and retrieval services.
• HPC & AI converged workloads.
• Agentic AI and generative AI back-ends.
• Sovereign AI deployments in facilities that cannot support liquid cooling.

AI workload positioning

The honest middle of the server range. H200 NVL is the PCIe form of H200, bridged in pairs or quads by NVLink. It matters because it delivers 141 GB of HBM3e per GPU and NVLink-class GPU-to-GPU bandwidth into an air-cooled, standard-rack chassis — no SXM baseboard, no NVSwitch, no mandatory liquid loop. For a great many Indian server rooms that is the difference between a system that can be installed and one that cannot. Choose NVL over SXM when the facility, the serviceability model, or the budget rules out an eight-GPU liquid-cooled node — and accept that peak multi-node training throughput belongs to SXM.

Industry use cases

• Public sector & sovereign: NVLink-class capability in government facilities without water.
• BFSI & HFT: model training and low-latency inference inside the bank’s own DC.
• Healthcare & life sciences: imaging and genomics workloads under residency rules.
• Research & education: departmental training capability on an ordinary power budget.
• Manufacturing: plant-side training and inference without a datacenter build.

Performance & how to be sure

H200 NVL is capacity- and bandwidth-led rather than a peak-FLOPS play: 141 GB per GPU with NVLink bridging is what makes 70B–180B training and long-context serving practical in an air-cooled box. Rather than quote a tokens/sec figure that will not match your workload, RDP offers a “benchmark your model” 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.

Series & upgrade path

Below: the air-cooled RTX PRO 6000 servers (inference/LoRA-led, no NVLink). Beside it: 8× H200 NVL for double the density. Above: 4× and 8× H200/B200/B300 SXM nodes with NVSwitch, then rack-scale GB300. Identical CUDA and serving stack throughout.

On-prem vs cloud (TCO)

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.

Software & day-one readiness

Ships workload-ready: Ubuntu LTS or RHEL, NVIDIA driver + CUDA + cuDNN, container runtime, Kubernetes/Slurm integration. Standard serving stacks (vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo) supported, and NVIDIA AI Enterprise licensing can be bundled on request.

Power, thermal & acoustics

4U air-cooled chassis with redundant PSUs on standard rack PDU feeds. No CDU, no facility water — a rack thermal survey is still recommended before install. Exact wattage, BTU and dB(A) figures come from RDP bench measurement rather than estimates — ask for the site-readiness sheet with your quote.

Deployment, warranty & support

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 — typically 8–10 weeks, subject to GPU allocation.

Why RDP

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.

Buy with confidence

Use Request a Quote to reach an RDP solution architect for sizing, a benchmark-your-model session, financing options and a delivery plan. No obligation.

Specifications