DRACO 2× RTX PRO 6000 aiDAPTIV+ Workstation

Overview

Server-class model sizes without a server room. Two NVIDIA RTX PRO 6000 Blackwell cards — 192 GB aggregate VRAM — backed by a 2 TB aiDAPTIV+ NVMe cache and 512 GB of ECC memory, reaching the 70B–180B band that normally sends organisations shopping for an eight-GPU node.

Key highlights

• 2× NVIDIA RTX PRO 6000 Blackwell 96GB — 192 GB aggregate VRAM, the most memory available in a professional tower.
• 2 TB aiDAPTIV+ cache (2× 1TB U.2 enterprise NVMe) — reaches the 180B band.
• 512 GB DDR5 ECC RDIMM on Intel Xeon w9 — large-corpus preparation without spilling.
• 8 TB NVMe primary storage for multi-model estates and checkpoint history.
• 70B–180B fine-tuning and inference on two cards rather than eight.
• Still a tower: air-cooled, standard power, no rack or chilled-water dependency.
• Sovereign by construction — nothing leaves the premises, DPDP-aligned.

AI workload fit

• Fine-tuning 70B–180B models (LoRA/QLoRA) on private data.
• High-quality inference for business-critical applications.
• Large-scale RAG across an enterprise document estate.
• Advanced agentic AI with long context and tool use.
• Generative AI, computer vision, NLP & speech at production quality.
• Also viable as a pre-production staging box ahead of a GPU-server rollout.

AI workload positioning

The top of the desk-side ladder, and the honest alternative to a first GPU server for organisations that need model quality but not multi-tenant throughput. 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 (LoRA/QLoRA), and the final numbers are confirmed in a scoped proof-of-concept, not promised on a datasheet.

Industry use cases

• BFSI & HFT: large document-intelligence and research models on-premises.
• Defence & aerospace: air-gap-capable model development on classified data.
• Public sector & sovereign: departmental large-model capability under DPDP.
• Healthcare: multimodal clinical models on protected patient data.
• Research & education: principal-investigator-scale compute without cluster contention.

Performance & how to be sure

192 GB of aggregate VRAM plus a 2 TB NVMe cache moves the ceiling from “which model fits” to “which model you want” — but throughput at 180B depends heavily on precision, context length and offload ratio. 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

DRACO is the flagship tier. Below: QUASAR 2× RTX PRO 5000 (70B). Above: DRACO 4× RTX PRO 6000 (180B+), then RDP GPU servers (4× and 8× RTX PRO 6000, H200, B200/B300) for multi-tenant throughput and true multi-GPU training. Identical software 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, and the aiDAPTIV+ memory-management layer, which is PyTorch-compliant and needs no changes to your model code. The aiDAPTIVPro toolchain covers data ingest, RAG, fine-tune, monitor, validate and inference from one interface, so a team is productive on day one rather than week three.

Power, thermal & acoustics

Dual high-TDP GPUs in a tower chassis; a dedicated 230V circuit and a server-room or well-ventilated location are recommended for sustained runs. 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 4–6 weeks.

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