CARINA 2× L4 5G Edge AI Node

Overview

The CARINA 2× L4 5G Edge AI Node brings AI inference to the edge — a short-depth 1u ruggedized node with 2× NVIDIA L4 (48 GB GDDR6) built to run private, low-latency AI at the site where data is created with 5G connectivity for multi-access edge computing (MEC): a factory floor, retail store, hospital, branch, cell tower or 5G MEC site. Data never has to travel to the cloud; latency stays low and prompts stay in-house, in INR, on a GST invoice.

Engineered for deployment outside a primary data centre, it fits short-depth and edge racks, runs the same software stack as RDP’s core servers, and ships with redundant power and BMC/IPMI for lights-out remote management of a fleet of sites.

Key highlights

• 2× L4 · 48 GB GDDR6 — efficient AI inference and light fine-tuning at the edge.
• Short-depth 1U ruggedized — fits edge, retail and telco / 5G MEC racks where standard-depth servers won’t.
• Intel Xeon 6 + 128 GB DDR5 ECC — local pre/post-processing and orchestration without backhaul.
• 5G + 2× 25 GbE — 5G connectivity plus high-throughput Ethernet for MEC; data stays at the site.
• Redundant PSU, BMC/IPMI — remote power, console and health monitoring for unattended sites.
• Low-latency local inference — no round-trip to the cloud.
• Make-in-India OEM — predictable INR pricing, GST tax invoice (HSN 8471), pan-India onsite support for distributed estates, GeM-procurable.
• Fleet upgrade path — standardise the edge tier and scale the core with GPU servers and micro data centers.

AI workload fit (what it actually runs — honestly)

• Edge inference (primary): serve quantised models and multiple small models locally for low-latency AI.
• Vision & multimodal: real-time defect detection, surveillance analytics and multimodal inference on local camera/sensor feeds.
• RAG & agentic: private, on-site RAG and agent back-ends over local document stores.
• Engineering note: the NVIDIA L4 (24 GB, low-power, single-slot) is built for inference and light fine-tuning, not large-model training — it is the right tool for low-power, low-latency edge AI. For heavy training, use a GPU Server or rack-scale system at the core.

AI workload positioning

This sits at the deploy-at-the-edge stage: the node you put where data is generated to keep latency low and data resident. With 48 GB GDDR6 in a compact, remotely-managed chassis, it is sized to sustain real local inference where backhauling to the cloud is too slow, too costly, or not permitted.

Industry use cases

• Manufacturing — real-time vision QA on the line.
• Retail & logistics — in-store / in-warehouse vision and forecasting.
• Healthcare — on-site clinical inference, PHI never leaving the building.
• Telecom — AI at the 5G network edge (MEC) for low-latency services.
• Energy & utilities — inference at remote or rugged facilities.
• Smart cities — traffic, safety and sensor analytics at the edge.

Performance — and how to be sure

We don’t publish inflated peak numbers. The honest picture: 48 GB GDDR6 across 2× L4 is sized to serve quantised models and run real-time vision at the edge. Want certainty? Request a free benchmark of your models and feeds on this exact configuration before you buy; we’ll send back real latency and throughput for your workload.

Series & upgrade path

• CARINA (entry edge tier) — this.
• Edge ladder: L4 (low-power inference) → L40S (performance inference) → RTX PRO 6000 Blackwell (high-capacity edge).
• When to step up: centralise heavy training at the core; keep inference at the edge — talk to an architect about the hub-and-spoke topology.

On-prem vs cloud — the TCO case

At the edge, owning beats renting twice over: you remove both round-trip latency and cloud egress, and keep data resident at the site. RDP pricing is fixed in INR with a GST input-credit-eligible invoice — ask for a 3-year TCO comparison across your sites.

Software & day-one readiness

Ships pre-configured to serve: NVIDIA driver, CUDA, cuDNN, Docker and NVIDIA Container Toolkit, with vLLM / Triton / TensorRT-LLM and PyTorch on Ubuntu LTS. Optional fleet-management, managed inference-stack and observability for distributed estates.

Power, cooling & rack integration

A Short-depth 1U ruggedized node with redundant PSUs, sized for edge, telco and retail racks — specify site power and cooling. (Exact PSU rating, BTU, airflow, depth and operating-environment figures confirmed on the build sheet.) BMC/IPMI enables lights-out management of unattended sites.

Deployment, warranty & support

• Made to order, built and burned-in in India; lead time confirmed at quote.
• In the box: node, rails, power cables, quick-start, and the pre-installed AI software stack.
• Onsite warranty + AMC with pan-India coverage for distributed sites 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 an edge AI node, made to order — talk to an RDP solution architect, design the right edge topology and 3-year TCO, and benchmark your own models on it before you commit. Request a quote to begin.

Specifications