CARINA 1× RTX PRO 5000 Blackwell AI Workstation

Overview

The CARINA 1× RTX PRO 5000 Blackwell AI Workstation is RDP’s upper-entry, single-GPU AI machine on NVIDIA’s current Blackwell generation — one RTX PRO 5000 Blackwell with 48 GB of GDDR7 in a quiet, air-cooled desk-side tower. It gives a developer or small team a private, on-premises workstation with the memory headroom to fine-tune and serve genuinely useful models — with Blackwell FP4 for efficient inference, data kept in-house, INR pricing, and day-one readiness.

A workstation-class Intel Xeon W-2500 with ECC memory, 128 GB DDR5 and 2 TB NVMe keep the GPU fed for sustained iteration — the strongest single-GPU rung of CARINA before stepping into 96 GB or multi-GPU.

Key highlights

• Current-gen RTX PRO 5000 Blackwell (48 GB GDDR7) — Blackwell with FP4; ECC GPU memory; a large single-card pool.
• Intel Xeon W-2500 + 128 GB DDR5 ECC — error-correcting workstation platform for stable long runs.
• 2 TB NVMe — fast local datasets, checkpoints and weights; no egress fees.
• Quiet, air-cooled tower — office-friendly.
• On-prem data sovereignty — IP and data stay in-house; DPDP-friendly.
• Make-in-India OEM — INR pricing, GST invoice (HSN 8471), pan-India onsite support, GeM-procurable.
• Upgrade path — step up to RTX PRO 6000 Blackwell (96 GB) or to 2×/4× (QUASAR/DRACO).

AI workload fit

• Inference: serve quantised models up to ~34B (4-bit) on the 48 GB card; FP4 boosts throughput.
• Fine-tuning: QLoRA / LoRA up to ~20B; full fine-tuning of 7B-class models.
• Build & prototype: vision, NLP and RAG pipelines on the 2 TB NVMe; agentic-app development — locally.

Engineering note: a single 48 GB card keeps the setup simple. For models or batches beyond 48 GB, step up within the line.

AI workload positioning

This sits at the develop-and-fine-tune stage: a single-engineer workstation with enough memory to fine-tune mid-size models and serve them on-desk, on current-generation hardware — far cheaper to own than continuous cloud rental, and private by design.

Industry use cases

• Software & AI teams — local model development, eval and agentic prototyping.
• Research & higher-ed — NLP, vision and speech work.
• Startups — a private fine-tuning box avoiding per-hour cloud costs.
• Manufacturing / healthcare / BFSI — on-prem vision, imaging and document-AI development.

Performance — and how to be sure

No inflated peak numbers: a single 48 GB RTX PRO 5000 Blackwell is sized for QLoRA up to ~20B and inference of quantised models up to ~34B. Request a free benchmark of your model on this exact configuration before you buy.

Series & upgrade path

• CARINA (1× — this) · QUASAR (2×) · DRACO (4×).
• Within CARINA: RTX PRO 6000 Blackwell (96 GB) for the largest single-card memory.
• When to step up: models >48 GB or multi-model serving → QUASAR/DRACO — talk to an architect.

On-prem vs cloud — the TCO case

For sustained development, owning beats renting: a cloud GPU running continuously adds up within months, and on-prem removes egress fees while keeping data in-house. RDP pricing is fixed in INR with a GST input-credit-eligible invoice — ask for a 3-year TCO comparison.

Software & day-one readiness

Ships pre-loaded: NVIDIA driver, CUDA, cuDNN, Docker and the NVIDIA Container Toolkit, with PyTorch / TensorFlow on Ubuntu LTS. Optional managed AI-stack setup available.

Power, thermal & acoustics

A single ~300 W-class GPU plus the Xeon W runs on a standard office circuit, air-cooled and quiet. (Exact PSU rating, BTU and dB(A) confirmed on the build sheet.)

Deployment, warranty & support

• Made to order, built and burned-in in India; lead time confirmed at quote.
• In the box: workstation, power cable, quick-start, and the pre-installed AI software stack.
• Onsite warranty + AMC, pan-India, with 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

Talk to an RDP solution architect, get a configuration and TCO tailored to your workload, and benchmark your own model on it before you commit. Request a quote to begin.

Specifications