CARINA RTX 2000 Ada Edge AI Mini PC

Overview

The CARINA RTX 2000 Ada Edge AI Mini PC runs AI agents locally — a mini / sff system with 1× NVIDIA RTX 2000 Ada (16 GB GDDR6) built to run private LLM agents, RAG and automation on your own hardware, offline if needed. It puts agentic AI on the desk without sending prompts or data to the cloud — in INR, on a GST invoice.

Engineered for developers, prosumers and teams adopting local agentic AI, it pairs a modern Intel Core Ultra 7 with on-chip NPU and the GPU with fast memory and NVMe so local models and agent workflows respond instantly and keep data in-house.

Key highlights

• 1× RTX 2000 Ada · 16 GB GDDR6 — run quantised local LLMs (7B–14B) and agent workflows on-device.
• GPU + on-chip NPU — accelerated local inference with an AI-PC NPU for always-on agents.
• Intel Core Ultra 7 (with NPU) + 64 GB DDR5 — responsive orchestration for multi-step agents and RAG.
• 2 TB NVMe NVMe — local model store, vector DB and document corpus; no egress.
• Mini / SFF, 2.5 GbE + Wi-Fi 6E — compact desk-side or edge footprint.
• Private & offline-capable — prompts, data and models stay on-device; air-gappable.
• Make-in-India OEM — predictable INR pricing, GST tax invoice (HSN 8471), pan-India onsite support, GeM-procurable.
• Upgrade path — step up to higher-memory RTX PRO Blackwell agentic PCs, or an agentic inference server for many concurrent agents.

AI workload fit (what it actually runs — honestly)

• Local agents (primary): run quantised 7B–14B LLMs for private agentic workflows — tool use, RAG, automation and copilots.
• RAG & document AI: on-device retrieval over your local corpus and vector DB.
• Vision & speech: local CV, speech-to-text and multimodal inference.
• Engineering note: with 16 GB GDDR6 of GPU memory this runs quantised models up to ~14B comfortably; for larger models or many concurrent agents, step up to a higher-memory PC or an agentic inference server. It is built for responsive local agents, not large-model training.

AI workload positioning

This sits at the local-agent stage: the device that runs your AI agents privately, on the desk. With 16 GB GDDR6 and a GPU plus NPU, it is sized to sustain responsive local inference and agent loops — where sending every prompt to a cloud API is slow, costly or non-compliant.

Industry use cases

• Software & product teams — private coding copilots and local agent development.
• BFSI & legal — confidential document AI and agents under data-residency rules.
• Healthcare — on-device clinical assistants keeping PHI local.
• Design & media — local creative and generative AI.
• Government & defence — air-gapped private agents.
• Education & research — a personal local-AI machine.

Performance — and how to be sure

We don’t publish inflated peak numbers. The honest picture: 16 GB GDDR6 is sized to run quantised 7B–14B models and agent loops locally with low latency. Want certainty? Request a free benchmark of your agents and models on this exact configuration before you buy; we’ll send back real tokens/sec and latency for your workload.

Series & upgrade path

• CARINA (entry local-AI tier) — this.
• Ladder: mini/edge → agentic AI PC → multi-GPU agentic PC → agentic inference server (many concurrent agents).
• When to step up: for many users or larger models, move to a higher-memory PC or an agentic inference server — talk to an architect.

On-prem vs cloud — the TCO case

Running agents locally removes per-token API costs and keeps data on-device: for steady agentic use, a one-time machine beats a recurring cloud bill, with full privacy. RDP pricing is fixed in INR with a GST input-credit-eligible invoice — ask for a cost-per-agent comparison vs cloud APIs.

Software & day-one readiness

Ships ready to run agents: NVIDIA driver, CUDA, cuDNN, Docker, with Ollama / vLLM, a local vector DB and popular agent frameworks pre-configured on Ubuntu or Windows. Optional managed local-AI stack and model library.

Power, cooling & rack integration

A mini / sff system — quiet desk-side operation. (Exact power draw, thermal and acoustic figures confirmed on the build sheet.) Standard mains power; no special facility needs.

Deployment, warranty & support

• Made to order, built and burned-in in India; lead time confirmed at quote.
• In the box: system, power supply, quick-start, and the pre-installed local-AI software stack.
• Onsite warranty + AMC with pan-India coverage and an RMA/escalation path (exact terms 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 local-AI machine for running agents privately, made to order — talk to an RDP solution architect, size it for your agents and models, and benchmark your own workload on it before you commit. Request a quote to begin.

Specifications