DRACO 8× B200 SXM aiDAPTIV+ AI Server

Overview

Blackwell, eight-wide, on your own floor. Eight NVIDIA B200 SXM GPUs joined by NVSwitch — 1.5 TB of HBM3e in a single memory domain — with a 16 TB aiDAPTIV+ cache. This is the node organisations buy when 405B-class training has to happen on-premises rather than on rented capacity.

Key highlights

• 8× NVIDIA B200 SXM 192GB HBM3e — 1.5 TB in one NVSwitch domain.
• Native FP8 Blackwell tensor cores — the precision most frontier models now ship in.
• NVSwitch all-to-all interconnect for genuine large-scale distributed training.
• 16 TB aiDAPTIV+ cache keeps additional models and long-context state resident.
• 2 TB DDR5 ECC dual-Xeon host; 8× 400G OSFP for cluster scale-out.
• Scales out: several nodes form a training cluster over InfiniBand or Spectrum-X Ethernet.
• Sovereign-capable: full training on-soil, DPDP-aligned, air-gap possible.

AI workload fit

• Large-scale LLM training and full fine-tuning at 405B class.
• High-throughput inference with FP8 for production services.
• Model fine-tuning across the full parameter set, not only adapters.
• HPC & AI converged simulation and modelling.
• Sovereign AI national and institutional programmes.
• Agentic AI and generative AI at scale.

AI workload positioning

The workhorse of serious on-premises AI. Eight Blackwell GPUs in one NVSwitch domain is the standard unit of frontier-adjacent capability, and the unit that scales cleanly into a multi-node cluster. aiDAPTIV+ extends usable model memory onto high-endurance enterprise NVMe. At this tier it is not a substitute for HBM — it is what keeps additional large models and long-context state resident without adding nodes. It remains a capacity technology; sustained throughput is confirmed in a scoped proof-of-concept.

Industry use cases

• Public sector & sovereign: national-scale model training held entirely on-soil.
• Neocloud: the standard sellable unit of GPU capacity for multi-tenant services.
• Research & education: institutional frontier-adjacent research capability.
• BFSI & HFT: proprietary large-model training with strict data control.
• Defence & aerospace: air-gapped training on classified corpora.

Performance & how to be sure

B200 with native FP8 and NVSwitch is a training-class node; published peak figures are NVIDIA’s and describe ideal conditions, so we size against your actual parallelism strategy and dataset. 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 SXM (entry NVLink) and the air-cooled RTX PRO servers. Above: 8× B300 SXM, then rack-scale GB300 NVL72 and multi-rack superclusters. Multiple B200 nodes cluster over InfiniBand/Spectrum-X for larger training runs.

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, NVIDIA Dynamo) supported.

Power, thermal & acoustics

8U SXM chassis at high power density with redundant PSUs; liquid-ready and strongly recommended with rear-door or direct-liquid cooling. A facility power and thermal survey is part of the quote, not an afterthought. 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 10–14 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