CARINA 1× RTX PRO 6000 Blackwell AI Workstation

Overview

The CARINA 1× RTX PRO 6000 Blackwell AI Workstation is RDP’s big-memory single-GPU AI machine on NVIDIA’s current Blackwell generation — one RTX PRO 6000 Blackwell with a full 96 GB of GDDR7 in a quiet, air-cooled desk-side tower. It is the most capable single-card workstation in the line: enough memory to fine-tune and serve genuinely large models locally, without multi-GPU complexity — with Blackwell FP4 for efficient inference, data kept in-house, INR pricing, and day-one readiness.

A workstation-class Intel Xeon W-3500 with ECC memory, 128 GB DDR5 and 4 TB NVMe keep that 96 GB GPU fed for sustained work — the top of CARINA before stepping into multi-GPU QUASAR/DRACO.

Key highlights

• 96 GB on a single RTX PRO 6000 Blackwell — one of the largest single-card pools available; fine-tune and serve large models with no partitioning.
• Blackwell + FP4 — current-generation architecture with efficient low-precision inference; ECC GPU memory.
• Intel Xeon W-3500 + 128 GB DDR5 ECC — high-core workstation platform with error-correcting memory.
• 4 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.

AI workload fit

• Inference: serve quantised models up to ~70B (4-bit) on the single 96 GB card; FP4 boosts throughput.
• Fine-tuning: QLoRA / LoRA up to ~34–70B; full fine-tuning of 7B–13B models.
• Build & serve: vision, multimodal, RAG and agentic workloads — locally, with no cloud bill.

Engineering note: 96 GB on one card avoids multi-GPU partitioning entirely — the simplest way to work with large models. For more throughput or parallel jobs, step up to 2×/4× in QUASAR/DRACO.

AI workload positioning

This sits at the fine-tune-and-deploy stage in a single box: enough memory for large-model work on-desk, far cheaper to own than a cluster, and private by design. The natural choice when one big GPU beats juggling several.

Industry use cases

• Software & AI teams — large-model fine-tuning and agentic development on one box.
• Research & higher-ed — NLP, multimodal and speech research.
• Healthcare & BFSI — on-prem fine-tuning of larger domain models under data-residency rules.
• Media & design — generative and rendering pipelines.

Performance — and how to be sure

No inflated peak numbers: a single 96 GB RTX PRO 6000 Blackwell is sized for QLoRA up to ~70B and inference of quantised 70B-class models. Request a free benchmark of your model on this exact configuration before you buy.

Series & upgrade path

• CARINA (1× — this, the big-memory single-GPU) · QUASAR (2×) · DRACO (4×).
• When to step up: need more throughput or parallel jobs → QUASAR/DRACO — talk to an architect.

On-prem vs cloud — the TCO case

For sustained large-model work, owning a 96 GB GPU beats renting one: continuous cloud time adds up fast, 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 and inference servers (vLLM / Triton) on Ubuntu LTS. Optional managed AI-stack setup available.

Power, thermal & acoustics

A single 600 W-class GPU plus the Xeon W — specify an appropriate circuit; air-cooled and quiet at the desk. (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