DRACO 4× RTX PRO 4500 Blackwell AI Workstation

Overview

The DRACO 4× RTX PRO 4500 Blackwell AI Workstation is RDP’s flagship desk-side machine at the accessible end of the 4-GPU tier — four NVIDIA RTX PRO 4500 Blackwell GPUs and 128 GB of next-generation GDDR7 in a single quiet, liquid-cooled tower. It puts a private, on-premises alternative to four rented cloud GPUs under one desk: your models and data never leave the building, costs are fixed in INR, and the box is productive on day one.

Built for AI/ML teams, research labs and product-engineering groups standardising on a repeatable, secure local-AI platform, it balances 128 GB of aggregate Blackwell GPU memory, a high-core Intel Xeon W-3500 data-prep engine, 256 GB of ECC system memory and 8 TB of NVMe — the difference between a workstation that benchmarks well and one that sustains real training and serving.

Key highlights

• 128 GB of GPU memory across 4× RTX PRO 4500 Blackwell — fine-tune and serve large models locally; run several models at once without queueing for shared cloud capacity.
• Blackwell architecture with FP4 support — next-gen inference efficiency and accuracy, ECC throughout for long, stable fine-tune runs.
• Intel Xeon W-3500 + 256 GB DDR5 ECC — a high-core data-prep, tokenisation and orchestration engine so the four GPUs aren’t starved.
• 8 TB NVMe — fast local datasets, checkpoints and model weights; no egress fees, no network bottleneck.
• Liquid-cooled, desk-side — sustained all-GPU clocks under load, quiet enough 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 DRACO to 4× RTX PRO 5000/6000 Blackwell, or to a rack-scale RDP system when you outgrow four GPUs.

AI workload fit (what it actually runs — honestly)

• Inference: serve quantised models up to ~70B (4-bit ≈ 40 GB fits on a single card) and run multiple quantised 7B–34B models concurrently across the four GPUs.
• Full-precision models: a 70B model in FP16 (~140 GB) exceeds 128 GB — run it quantised here, or step up to the 192 GB+ DRACO tiers for FP16.
• Fine-tuning: QLoRA / LoRA up to ~70B, and full fine-tuning of 7B–13B models, data-parallel across the four GPUs.
• Vision, multimodal, RAG & agentic: train/serve vision and multimodal models, build RAG pipelines on the 8 TB NVMe, and run multi-agent workflows locally.
• Engineering note: the RTX PRO 4500 Blackwell workstation card has no NVLink, so the four cards run over PCIe — ideal for data-parallel training and multi-model serving; for one model larger than 32 GB, use tensor parallelism across the cards. This is the right tool for fine-tuning + inference at team scale, not 1000-GPU pre-training.

AI workload positioning

This sits at the fine-tune-and-deploy stage of the AI lifecycle: heavier than a 1–2 GPU workstation, lighter and far cheaper to own than a rack-scale cluster. With 128 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 simulation 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/rendering pipelines.
• Research & higher-ed — a shared lab AI 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: 128 GB of aggregate Blackwell GPU memory and four GPUs are sized to fine-tune up to ~70B (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) · DRACO (flagship, 4× GPU — this).
• Within DRACO: step up to 4× RTX PRO 5000 (192 GB) or 4× RTX PRO 6000 Blackwell (384 GB) for more memory and FP16 headroom.
• When to step up: beyond four GPUs or for trillion-parameter training, move to an RDP rack-scale GPU server / NVL-class 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: four 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

Four GPUs plus the Xeon W-3500 draw real power — specify a dedicated circuit. The system is liquid-cooled to hold full 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 flagship, 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