DRACO 4× RTX PRO 6000 aiDAPTIV+ Workstation

Overview

384 GB of VRAM in a tower — the largest models an organisation can run without building a server room. Four NVIDIA RTX PRO 6000 Blackwell cards, 1 TB of ECC memory and a 2 TB aiDAPTIV+ NVMe cache take the 180B+ class on-premises, entirely under your own control.

Key highlights

• 4× NVIDIA RTX PRO 6000 Blackwell 96GB — 384 GB aggregate VRAM.
• 2 TB aiDAPTIV+ cache extends usable model memory beyond the 384 GB of silicon.
• 180B+ fine-tuning and inference — a class most organisations assume requires a datacenter node.
• 1 TB DDR5 ECC RDIMM (8× 128GB) on Intel Xeon w9 — no data-prep bottleneck.
• 8 TB NVMe primary storage for large corpora and full checkpoint lineage.
• Model-parallel across four cards; no NVSwitch required for this workload class.
• Air-cooled tower: no rack, no chilled water, no data-centre build.

AI workload fit

• Fine-tuning 180B+ models with LoRA/QLoRA on proprietary corpora.
• High-quality production inference for mission-critical applications.
• Enterprise-scale RAG with long context windows.
• Complex agentic AI and multi-step reasoning workloads.
• Sovereign AI work requiring full air-gap capability.
• Not a substitute for a multi-node cluster on frontier pretraining.

AI workload positioning

The ceiling of the desk-side ladder and the last rung before a GPU server. Choose this when model quality matters more than multi-tenant throughput, and when the data cannot leave the building at any price. 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

• Defence & aerospace: air-gapped large-model development on classified material.
• Public sector & sovereign: national-language and policy models held entirely on-soil.
• BFSI & HFT: large proprietary research and risk models with zero external exposure.
• Healthcare: multimodal clinical AI on protected patient data.
• Research & education: frontier-adjacent research without cluster allocation politics.

Performance & how to be sure

With 384 GB VRAM plus a 2 TB cache, capacity stops being the limit and precision/parallelism strategy becomes the design question — exactly the thing a paper spec cannot answer for you. 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

Top of the DRACO workstation line. Below: DRACO 2× RTX PRO 6000 (70B–180B). Above: RDP GPU servers — QUASAR 4× RTX PRO 6000, DRACO 8× RTX PRO 6000, then H200 and B200/B300 SXM nodes for multi-tenant serving 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. Standard serving stacks (vLLM, SGLang, TensorRT-LLM) are supported.

Power, thermal & acoustics

Four high-TDP GPUs in a tower chassis — a dedicated high-capacity 230V circuit and a server-room location with proper airflow are required, not optional, 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 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