CARINA 1× RTX PRO 5000 aiDAPTIV+ Workstation

Overview

The entry point to serious on-premises AI — one GPU, models that normally need several. A single NVIDIA RTX PRO 5000 Blackwell paired with a 640 GB aiDAPTIV+ NVMe cache lets a developer fine-tune and serve 13B–34B models at their own desk, behind the firewall, with no cloud GPU meter running.

Key highlights

• 1× NVIDIA RTX PRO 5000 Blackwell 48GB GDDR7 — professional-class compute with ECC throughout.
• 640 GB aiDAPTIV+ cache (2× M.2 enterprise NVMe) — extends usable model memory well past the 48 GB of on-card VRAM.
• Fine-tune 13B–34B locally with LoRA/QLoRA — the class of work most Indian enterprises actually start with.
• 128 GB DDR5 ECC RDIMM — headroom for data preparation and multi-model experimentation.
• 2 TB NVMe primary storage for datasets, checkpoints and container images.
• Runs on a standard office circuit — no rack, no dedicated cooling, no facility work.
• Your data never leaves the building — DPDP-aligned by architecture, not by policy promise.

AI workload fit

• Fine-tuning 13B–34B models with LoRA/QLoRA on proprietary data.
• Inference and model serving for a team or department.
• RAG pipelines over private document sets.
• Agentic AI development and iteration.
• Computer vision and NLP & speech prototyping.
• Not for large-scale pretraining — that is cluster work (see the upgrade path).

AI workload positioning

This is the first rung of the ladder: the machine an organisation buys when cloud GPU spend has become predictable enough to own instead of rent, but a multi-GPU server is not yet justified. 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: fine-tune on transaction and policy data that cannot leave the premises under DPDP.
• Healthcare: local work on patient records and clinical notes with residency preserved.
• Enterprise & GCCs: a private development box per AI engineer instead of contended cloud credits.
• Research & education: per-lab AI compute inside a departmental budget.
• Manufacturing: defect-detection and quality-inspection model development close to the line.

Performance & how to be sure

Capability here is set by aiDAPTIV+ memory extension rather than raw FLOPS: the constraint that normally stops a 34B model running on one card is capacity, and that is precisely what the NVMe cache addresses. 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

CARINA is the entry tier. When one GPU is no longer enough: step to the QUASAR 2× RTX PRO 4500 (34B–70B), then QUASAR 2× RTX PRO 5000 (70B), then the DRACO workstations (90B–180B), and finally an RDP GPU server. The CUDA and aiDAPTIV+ software stack is identical at every rung, so nothing is rewritten when you scale.

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

Single-socket tower on a standard 230V office circuit, air-cooled, sized for a desk-side or small server-room location. 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