QUASAR 2× RTX PRO 5000 aiDAPTIV+ Workstation

Overview

70B-class models, fine-tuned at your desk, on two GPUs. Dual NVIDIA RTX PRO 5000 Blackwell cards (96 GB aggregate) with a 1.32 TB aiDAPTIV+ NVMe cache put the 70B tier — the size most enterprises standardise on once they leave pilots — inside a tower you own, power from a normal circuit, and never send to a cloud.

Key highlights

• 2× NVIDIA RTX PRO 5000 Blackwell 48GB — 96 GB aggregate VRAM.
• 1.32 TB aiDAPTIV+ cache (1TB U.2 + 320GB M.2 enterprise NVMe).
• 70B fine-tuning (LoRA/QLoRA) plus BF16 inference — without a GPU server.
• 256 GB DDR5 ECC RDIMM on Intel Xeon w7 for heavy data preparation.
• 4 TB NVMe primary storage for corpora, checkpoints and model versions.
• Tower form factor, air-cooled — no rack, no facility chilled water.
• Full data sovereignty: training and inference stay behind your firewall.

AI workload fit

• Fine-tuning 70B-class models with LoRA/QLoRA on proprietary data.
• Departmental inference and model serving at BF16/INT4.
• Large-corpus RAG with private embeddings.
• Agentic AI pipelines and tool-using assistants.
• Generative AI, computer vision, NLP & speech.
• Not for pretraining a foundation model from scratch.

AI workload positioning

The point on the ladder where a workstation genuinely replaces a small server for most teams. 70B is where enterprise accuracy expectations are typically met, and aiDAPTIV+ is what makes that size reachable on two professional cards instead of an eight-GPU node. 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: 70B document-intelligence and risk models tuned on in-house records.
• Healthcare: clinical summarisation and coding models kept fully on-premises.
• Public sector & sovereign: departmental AI with data-residency guarantees.
• Research & education: serious fine-tuning without queueing for shared cluster time.
• Enterprise & GCCs: the platform team’s reference box before scaling to servers.

Performance & how to be sure

At this tier the binding constraint is memory capacity plus offload bandwidth, not raw TFLOPS — a 70B model at 4-bit behaves very differently from BF16, so a paper figure would mislead. 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 tier. Below: QUASAR 2× RTX PRO 4500 (34B–70B) and CARINA 1× RTX PRO 5000 (13B–34B). Above: DRACO 2× RTX PRO 6000 (70B–180B) and DRACO 4× RTX PRO 6000 (180B+), then RDP GPU servers. The CUDA + aiDAPTIV+ stack is identical at every rung.

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-GPU tower, air-cooled, single 230V circuit; server-room placement recommended for sustained multi-day fine-tuning 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 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