DRACO 8× H200 NVL AI Server

Overview

1.1 TB of HBM3e, eight GPUs, NVLink-bridged — and still air-cooled. Eight NVIDIA H200 NVL cards arranged as two 4-way NVLink domains in an 8U chassis: the maximum HBM capacity RDP can deliver without committing your facility to liquid cooling.

Key highlights

• 8× NVIDIA H200 NVL 141GB HBM3e — 1.128 TB of high-bandwidth memory.
• 2× 4-way NVLink domains — high GPU-to-GPU bandwidth within each quad.
• Air-cooled 8U — the density ceiling before liquid cooling becomes mandatory.
• 1 TB DDR5 ECC dual-Xeon host; 4× 100GbE plus BMC/Redfish.
• Individually serviceable PCIe cards — no whole-baseboard RMA event.
• Scales out over Ethernet or InfiniBand into a multi-node cluster.
• Fully on-premises, DPDP-aligned, air-gap capable.

AI workload fit

• LLM training and full fine-tuning at 180B–405B class.
• High-concurrency, long-context inference services.
• Enterprise-scale RAG with large resident indices.
• HPC & AI convergence needing large memory per node.
• Agentic AI platforms serving many concurrent sessions.
• Sovereign AI where facility constraints rule out SXM.

AI workload positioning

The largest air-cooled HBM node in the 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. Two 4-way NVLink domains is not the same as an 8-GPU NVSwitch all-to-all fabric — for single large training jobs an SXM node will win, and we will say so. For memory-bound inference, long context and multi-model serving, this configuration is frequently the better and far more deployable buy.

Industry use cases

• Public sector & sovereign: maximum on-soil capability within existing facilities.
• Neocloud: dense air-cooled capacity where DC water is unavailable or costly.
• BFSI & HFT: large-model training plus production inference in one node.
• Healthcare: hospital-group AI platforms with strict data residency.
• Research & education: institutional compute without a facility upgrade programme.

Performance & how to be sure

1.128 TB of HBM3e across eight cards makes this a memory-capacity leader among air-cooled nodes; interconnect topology (two quads, not one octet) is the trade, and it is the right trade for inference-led and memory-bound workloads. 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: 4× H200 NVL. Beside: the air-cooled 8× RTX PRO 6000 (aiDAPTIV+ capacity play). Above: 8× B200/B300 SXM with NVSwitch for single-job training scale, then rack-scale GB300 NVL72 and superclusters.

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

8U air-cooled chassis with redundant PSUs at high rack density. Standard PDU feeds, no CDU required, but a rack-level airflow and power survey is strongly recommended before installation. 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 10–12 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