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Solution Overview · AI Computing & GPU Infrastructure

The compute backbone for AI workloads.

Narali supplies, configures, and deploys GPU servers, AI workstations, edge inference appliances, and AI-optimized storage for machine learning, computer vision, video analytics, and private LLM workloads.

GPU training clustersAI workstationsEdge inferenceNVMe storageOn-prem & data sovereignty

On-prem

Full control over data and models

NVLink

InfiniBand multi-GPU interconnect

Edge

Real-time inference appliances

Sovereign

Data stays inside your infrastructure

Architecture

How the system fits together.

Edge lane devices, control and data, and management integration work as one platform — with every event pushed to the command center.

GPU Compute Tiers

On-Premise AI · Full Control

GPU Training ClusterH100 · A100 · H200
AI WorkstationRTX · L40S · Ada

Edge & Data Layer

Edge AI ApplianceJetson Orin · AGX
AI-Optimized StorageNVMe · High-throughput I/O

AI Computing & GPU Infrastructure

What we deliver

The implementation scope is shaped around the architecture, target service level, operating model, and handover responsibilities.

01

Multi-GPU rack servers with NVLink and InfiniBand interconnect.

02

Professional AI workstations for data science and computer vision teams.

03

Compact edge inference appliances for real-time video analytics.

04

NVMe all-flash storage for dataset I/O, checkpoints, and inference serving.

Where it is deployed

Keep your models, data, and inference under your control.

Sensitive data, models, and inference stay inside the client's own infrastructure where control, latency, and data sovereignty matter.

LLM training & fine-tuning

Multi-GPU clusters for training and fine-tuning large models on private data.

Private / on-prem LLM

Self-hosted inference so prompts and data never leave your environment.

Computer vision

Workstations and GPU servers for detection, classification, and segmentation.

Video analytics

Edge appliances running real-time analytics on live camera streams.

Research & HPC

High-throughput compute and storage for simulation and data science teams.

Edge inference

Compact Jetson-class appliances for low-latency inference at remote sites.

Delivery model

From GPU sizing to a running AI platform.

01

Design

Model and workload profiling, GPU sizing, interconnect, storage, and power design.

02

Deploy

Supply, rack, configure drivers, frameworks, orchestration, and validate throughput.

03

Operate

Monitor utilization, thermals, and jobs; maintain drivers and capacity headroom.

04

Handover

Environment documentation, benchmarks, operating routine, and escalation path.

Next Step

Scope your AI platform around your models and data.

Share the models, datasets, latency needs, and sovereignty requirements. Narali will size the GPU architecture and delivery plan.

Contact Narali