DRACO 4× H200 SXM aiDAPTIV+ AI Server

Overview

Real NVLink bandwidth, at half the node. Four NVIDIA H200 SXM GPUs — 564 GB of HBM3e joined by NVLink — with an 8 TB aiDAPTIV+ cache. The entry point to genuine multi-GPU training for organisations that need the interconnect but not yet the power envelope of a full eight-GPU node.

Key highlights

• 4× NVIDIA H200 SXM 141GB HBM3e — 564 GB of high-bandwidth memory.
• NVLink interconnect — true multi-GPU training, not just model-parallel inference.
• 4.8 TB/s per-GPU memory bandwidth class — the reason SXM exists.
• 8 TB aiDAPTIV+ cache extends resident capacity beyond HBM for large-context work.
• 1 TB DDR5 ECC dual-Xeon host with 2× 400G fabric-ready networking.
• Half the power and heat of an 8-GPU SXM node — easier to site in Indian facilities.
• On-premises and DPDP-aligned; air or liquid-assisted cooling options.

AI workload fit

• Genuine multi-GPU training and full fine-tuning (not just LoRA).
• Inference for 180B–405B models with long context.
• Distributed RAG and retrieval at production scale.
• HPC & AI converged workloads needing FP64 alongside AI.
• Agentic AI and generative AI services.
• Sovereign AI programmes requiring on-soil training capability.

AI workload positioning

The crossover node. Below this, aiDAPTIV+ and PCIe cards handle inference and LoRA superbly; at this rung you gain NVLink, and with it the ability to actually train rather than only adapt. For many Indian enterprises and research institutions this is the right first SXM purchase — full 8-GPU nodes are frequently over-bought. aiDAPTIV+ extends usable model memory onto high-endurance enterprise NVMe, so a model class that would normally demand far more GPUs runs on the GPUs you actually own. It is a capacity technology, not a bandwidth one — streaming offload is tuned for inference and parameter-efficient fine-tuning, and the final numbers are confirmed in a scoped proof-of-concept, not promised on a datasheet.

Industry use cases

• Research & education: institutional training capability on a realistic power budget.
• Public sector & sovereign: on-soil model training for national language and policy work.
• BFSI & HFT: proprietary model training with low-latency inference alongside.
• Healthcare & life sciences: genomics and imaging models needing FP64 plus AI.
• Neocloud: a right-sized unit of capacity for multi-tenant GPU services.

Performance & how to be sure

H200 SXM is bandwidth-led: 141 GB of HBM3e per GPU with NVLink is what makes training and long-context inference practical, and aiDAPTIV+ extends what stays resident beyond that. 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). Above: 8× H200 SXM, then B200/B300 SXM nodes, and rack-scale GB300 NVL72 or AMD systems for frontier work. Same CUDA and serving stack at every rung.

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, and the aiDAPTIV+ memory-management layer — PyTorch-compliant, no model-code changes. Standard serving stacks (vLLM, SGLang, TensorRT-LLM) supported, plus the aiDAPTIVPro toolchain for ingest, RAG, fine-tune, monitor, validate and inference.

Power, thermal & acoustics

4U SXM node with redundant PSUs; air-cooled with liquid-assist available. Materially lower power and thermal load than an 8-GPU SXM chassis — often the deciding factor in existing Indian server rooms. 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