QUASAR 2× RTX PRO 4500 Blackwell AI Workstation

Overview

The QUASAR 2× RTX PRO 4500 Blackwell AI Workstation is RDP’s performance-tier desk-side machine — two NVIDIA RTX PRO 4500 Blackwell GPUs and 64 GB of next-generation GDDR7 GPU memory in a single quiet tower, built for teams that have outgrown a single-GPU developer box and want to fine-tune and serve real models on-premises. It gives AI/ML engineers a private, fixed-cost alternative to renting two cloud GPUs: your models and data stay in the building, billing is in INR, and the system is productive on day one.

Engineered for product-engineering groups, research labs and applied-AI teams standardising on a repeatable local-AI platform, it balances 64 GB of aggregate Blackwell GPU memory, an Intel Xeon W-2500 data-prep engine, 128 GB of ECC system memory and 4 TB of NVMe — so the GPUs stay fed and the box sustains real training and serving, not just benchmarks.

Key highlights

• 64 GB of GPU memory across 2× RTX PRO 4500 Blackwell — fine-tune mid-size models and serve several models concurrently without queueing for shared cloud capacity.
• Blackwell-generation architecture with FP4 support — next-gen inference efficiency and accuracy, ECC throughout for long, stable fine-tune runs.
• Intel Xeon W-2500 + 128 GB DDR5 ECC — a serious tokenisation, data-prep and orchestration engine so the GPUs are never starved.
• 4 TB NVMe — fast local datasets, checkpoints and weights; no egress fees, no network bottleneck.
• Quiet dual-GPU tower — sized for an office, not a server room.
• On-prem data sovereignty — IP and customer data 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.
• Upgrade path — step up within QUASAR to RTX PRO 5000/6000 Blackwell, or to a DRACO 4-GPU flagship when you outgrow two cards.

AI workload fit (what it actually runs — honestly)

• Inference: serve quantised models up to ~70B (4-bit ≈ 40 GB fits across the two cards) and run multiple quantised 7B–34B models concurrently.
• Fine-tuning: QLoRA / LoRA up to ~34B, and full fine-tuning of 7B-class models, data-parallel across both GPUs.
• Vision, multimodal, RAG & agentic: train/serve vision and multimodal models, build RAG pipelines on the 4 TB NVMe, and run multi-agent workflows locally.
• Engineering note: the RTX PRO 4500 Blackwell workstation card has no NVLink — the two cards run over PCIe, ideal for data-parallel training and multi-model serving; for a single model larger than 32 GB, use tensor parallelism across the pair. This is the right tool for team-scale fine-tune-and-serve, not 1000-GPU pre-training.

AI workload positioning

This sits at the fine-tune-and-deploy stage of the AI lifecycle: heavier than a single-GPU developer workstation, lighter and far cheaper to own than a rack-scale cluster. With 64 GB of Blackwell GPU memory, a high-core Xeon W, ECC memory and fast NVMe in balance, it is built to sustain real fine-tuning and high-throughput inference — the workloads where shared cloud capacity gets expensive and slow.

Industry use cases

• Manufacturing — defect-detection vision models and digital-twin experiments on the factory floor.
• Healthcare & life sciences — fine-tune medical LLMs / imaging models on-prem, keeping PHI in-house.
• BFSI — private fraud, risk and document-intelligence models under data-residency rules.
• Media & design — generative image/video and 3D pipelines.
• Research & higher-ed — a shared lab workstation for NLP, speech and multimodal research.
• Software / product teams — local fine-tuning, eval and agentic-app development without cloud bills.

Performance — and how to be sure

We don’t publish inflated peak numbers. The honest picture: 64 GB of aggregate Blackwell GPU memory and two GPUs are sized to fine-tune up to ~34B (QLoRA) and serve quantised 70B-class models on-prem. Want certainty? Request a free benchmark of your model and dataset on this exact configuration before you buy — we’ll send back real tokens/sec and fine-tune timings for your workload.

Series & upgrade path

• CARINA (entry, 1× GPU) · QUASAR (performance, 2× GPU — this) · DRACO (flagship, 4× GPU).
• Within QUASAR: step up to 2× RTX PRO 5000 (96 GB) or 2× RTX PRO 6000 Blackwell (192 GB) for more memory and throughput.
• When to step up: beyond two GPUs, move to a DRACO 4-GPU workstation or an RDP rack-scale system — talk to an architect for the migration path.

On-prem vs cloud — the TCO case

For sustained fine-tuning and inference, owning beats renting: two equivalent cloud GPUs running continuously add up fast, and on-prem removes egress fees and keeps data in-house. RDP pricing is fixed in INR with a GST input-credit-eligible invoice — ask for a 3-year TCO comparison against your current cloud spend.

Software & day-one readiness

Ships pre-loaded and ready to train: NVIDIA driver, CUDA, cuDNN, Docker and NVIDIA Container Toolkit, with PyTorch / TensorFlow and common inference servers (vLLM / Triton) configured on Ubuntu LTS. Optional managed AI-stack and model-zoo setup available.

Power, thermal & acoustics

Two RTX PRO 4500 Blackwell GPUs plus the Xeon W draw real power — specify a dedicated circuit. The tower is air-cooled and tuned to hold clocks quietly at the desk. (Exact PSU rating, BTU and dB(A) figures confirmed on the build sheet.)

Deployment, warranty & support

• Made to order, built and burned-in in India; realistic lead time confirmed at quote.
• In the box: workstation, power cables, feet, quick-start, and the pre-installed AI software stack.
• Onsite warranty + AMC with pan-India coverage and an RMA/escalation path (exact term & response window 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 performance-tier, made-to-order system — talk to an RDP solution architect, get a configuration and 3-year TCO tailored to your workload, and benchmark your own model on it before you commit. Request a quote to begin.

Specifications