DRACO 80× RTX PRO 6000 Blackwell Multi-Node System

Overview

The DRACO 80× RTX PRO 6000 Blackwell Multi-Node System is a dense 20-blade chassis system that packs 80 RTX PRO 6000 Blackwell Server Edition GPUs (7,680 GB GDDR7) into one shared-infrastructure chassis with pooled power and cooling. It consolidates many inference and fine-tune workloads into one efficient footprint on-premises, in INR, on a GST invoice.

Engineered for platform teams who want density and serviceability, it shares power supplies, cooling and management across the blades, so you deploy more GPUs per rack-U with one management plane — delivered racked, cabled and validated as a single SKU with one warranty.

Key highlights

• 80× RTX PRO 6000 Blackwell · 7,680 GB GDDR7 — high GPU density for consolidated inference, fine-tuning and small-scale training.
• 20-blade chassis, shared power & cooling — more GPUs per rack-U with pooled PSUs and a single management plane.
• 40× Intel Xeon 6 (20 blades) + 20 TB DDR5 ECC — host compute and memory matched to 80 GPUs.
• 320 TB NVMe NVMe + InfiniBand NDR 400G — fast local storage and a low-latency InfiniBand fabric for multi-blade jobs.
• Hot-swap, serviceable — blade-level service, redundant shared PSUs, full BMC/IPMI.
• On-prem data sovereignty — data and models 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 — add blades, or step up to rack-scale systems and SuperClusters.

AI workload fit (what it actually runs — honestly)

• Consolidated inference (primary): host many models across 80 GPUs for high aggregate throughput with one management plane.
• Fine-tuning & small-scale training: data-parallel fine-tuning across blades.
• RAG, vision, multimodal & agentic: dense production AI services and multi-agent back-ends.
• Engineering note: the RTX PRO 6000 Blackwell Server Edition is a PCIe card with no NVLink; GPUs excel at data-parallel serving and multi-instance hosting. Across blades, the InfiniBand fabric carries collective traffic for distributed jobs.

AI workload positioning

This sits at the consolidate-and-serve stage: a density play that puts many GPUs in one serviceable chassis. With 7,680 GB GDDR7 across 80 GPUs, it is sized to sustain a fleet of inference and fine-tune workloads in one footprint — more efficient than many separate servers, simpler than a full rack-scale build.

Industry use cases

• Neocloud / AI providers — dense, multi-tenant GPU capacity per rack.
• BFSI — a consolidated private inference fleet under data-residency rules.
• SaaS / product — host many model endpoints in one managed system.
• Healthcare & research — shared departmental GPU density, data in-house.
• Manufacturing & retail — consolidated vision/inference services.
• Government & PSU — efficient sovereign GPU capacity on GeM.

Performance — and how to be sure

We don’t publish inflated peak numbers. The honest picture: 7,680 GB GDDR7 across 80 GPUs in one shared chassis is sized for consolidated inference and fine-tuning. Want certainty? Request a free benchmark of your models on this exact configuration before you buy; we’ll send back real tokens/sec, concurrency and density figures.

Series & upgrade path

• DRACO (flagship density tier) — this.
• Density ladder: twin → compute-block → blade-chassis; step up GPU count and choose SXM (H200) for distributed training.
• When to step up: for the largest tightly-coupled training, move to SXM rack-scale systems — talk to an architect.

On-prem vs cloud — the TCO case

For sustained GPU density, owning beats renting: a continuously-running cloud GPU fleet of this size adds up fast, and on-prem removes egress and keeps data in-house. Shared power/cooling lowers per-GPU operating cost. RDP pricing is fixed in INR with a GST input-credit-eligible invoice — ask for a 3-year TCO comparison.

Software & day-one readiness

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

Power, cooling & rack integration

A Dual Blade Chassis 16U system with shared, redundant PSUs and pooled cooling — specify rack power and cooling; liquid-cooling available on dense configs. (Exact PSU rating, BTU, airflow and rack-depth figures confirmed on the build sheet.) Single management plane via BMC/IPMI across the blades.

Deployment, warranty & support

• Made to order, built, racked and burned-in in India; lead time confirmed at quote.
• In the box: chassis/blades, 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 terms 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 dense multi-node GPU system, made to order — talk to an RDP solution architect, size the density, fabric and cooling for your workload, get a 3-year TCO, and benchmark your own models before you commit. Request a quote to begin.

Specifications