QUASAR 4× RTX PRO 6000 aiDAPTIV+ AI Server

Overview

The first real server rung — four GPUs, multi-tenant, and still air-cooled. A 4U node with four NVIDIA RTX PRO 6000 Blackwell cards and an 8 TB aiDAPTIV+ NVMe cache, built for teams that have outgrown a workstation but do not want liquid cooling, an NVSwitch fabric, or a datacenter retrofit.

Key highlights

• 4× NVIDIA RTX PRO 6000 Blackwell 96GB — 384 GB aggregate VRAM in 4U.
• 8 TB aiDAPTIV+ cache — serves several large models concurrently rather than one at a time.
• Dual Xeon host with 512 GB DDR5 ECC for heavy data pipelines.
• Multi-tenant serving: several teams or applications share one node with isolation.
• Air-cooled 4U — fits a standard rack; no facility water, no CDU.
• 25/100GbE networking for cluster-adjacent data movement.
• On-prem and DPDP-aligned — the data stays in your rack.

AI workload fit

• Multi-tenant inference serving for 70B–180B models.
• Fine-tuning (LoRA/QLoRA) as a shared department resource.
• Production RAG services with concurrent users.
• Agentic AI back-ends running continuous workloads.
• Computer vision and NLP & speech at service scale.
• Consolidating several workstations onto one managed, monitored node.

AI workload positioning

This is where AI stops being a project and becomes a service. A workstation serves a person; this serves an organisation — with the concurrency, remote management and rack discipline that implies, while staying inside an ordinary air-cooled rack. 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

• Enterprise & GCCs: a shared inference service behind the corporate firewall.
• BFSI: concurrent risk, fraud and document-intelligence services on-premises.
• Healthcare: hospital-wide clinical AI services under residency rules.
• Public sector & sovereign: departmental AI platform on state-owned infrastructure.
• Manufacturing: multi-line vision inference from a single plant-side node.

Performance & how to be sure

Concurrency, not peak throughput, is the metric that matters here — how many simultaneous sessions hold their latency target while the aiDAPTIV+ cache keeps several models resident. 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

QUASAR is the performance server tier. Below sit the DRACO workstations. Above: DRACO 8× RTX PRO 6000 (double the density), then H200 and B200/B300 SXM nodes with NVLink/NVSwitch for true multi-GPU training, and rack-scale systems beyond that.

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

4U air-cooled chassis with redundant power supplies; standard rack PDU feeds, no liquid cooling or CDU required — deployable in an existing server room. 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 6–8 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