NVIDIA DGX Spark AI supercomputer infrastructure

// OUR TECHNOLOGY

BUILT FOR ENTERPRISE AI

NVIDIA-powered infrastructure. On-premise deployment. Healthcare-grade security. Zero compromises.

1 PFLOP
FP4 AI Performance
200B+
Parameter Models
128 GB
Unified Memory
0
Data Leaves Your Building

// HARDWARE

NVIDIA DGX Spark

The world's most powerful compact AI supercomputer, deployed inside your building. Run large language models locally with zero data leaving your premises.

// ARCHITECTURE

Deployment Pipeline

From hardware to agents — every layer purpose-built for enterprise AI.

On-Premise Deployment

On-Premise Deployment

DGX Spark installed in your server room. Physical hardware under your control. Air-gapped option for maximum security environments.

Local LLM Inference

Local LLM Inference

vLLM serving 200B+ parameter models with continuous batching and optimised throughput. Llama, Mistral, and custom fine-tuned models.

Secure API Layer

Secure API Gateway

Authenticated, encrypted API layer. Role-based access control. Full audit logging. Zero external data transfer for sensitive workloads.

Department AI Agents

Department Agents

AI agents deployed across Sales, HR, Operations, Customer Service, Marketing, Lab, Finance, and Compliance. Each with domain-specific knowledge.

// SECURITY & COMPLIANCE

Enterprise-Grade Security

Built for healthcare. Built for compliance. Built for the most sensitive data on earth.

PDPA Compliance

PDPA Compliance

Built for Malaysia's Personal Data Protection Act from day one. Consent management, data minimisation, and audit trails built into every system.

Air-Gapped Deployment

Air-Gapped Deployment

Complete physical network isolation available. No internet connectivity required for core AI operations. Biometric access, 24/7 monitoring, tamper detection.

// AI STACK

Technology Stack

Every layer engineered for performance, security, and scale.

LLM Inference

LLM Inference — vLLM

High-throughput, memory-efficient serving of large language models with continuous batching. Optimised for Grace Blackwell architecture.

Voice AI

Voice AI — ElevenLabs

Natural, human-like voice synthesis for multilingual AI agents. English, Bahasa Malaysia, and Mandarin Chinese — real conversations, not IVR menus.

Vector Embeddings

Embeddings — On-Premise

Local embedding generation for document intelligence and semantic search. BGE-M3 multilingual embeddings with Qdrant vector store — all on your hardware.

RAG Pipeline

RAG Pipeline — Custom

Retrieval-augmented generation for document intelligence. SOPs, contracts, medical protocols, compliance docs — instantly searchable with source citations.

See Our Technology in Action

Book a demo and experience enterprise AI infrastructure first-hand.

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Frequently Asked Questions

NovaGenAI uses a full-stack NVIDIA AI ecosystem including CUDA, cuDNN, TensorRT, Triton Inference Server, NeMo, NIM, and RAPIDS. We also integrate with Google Cloud, AMD processors, and leading AI model providers including Anthropic, OpenAI, and ElevenLabs for voice AI.
NVIDIA DGX Spark is a desktop-class AI supercomputer delivering 1 petaflop of performance. NovaGenAI deploys DGX Spark for on-premise enterprise AI installations, enabling organisations to run large language models and inference workloads without sending data to the cloud — critical for regulated industries.
Yes. On-premise deployment is a core capability. We deploy AI infrastructure using NVIDIA DGX Spark and custom GPU clusters within client facilities, ensuring full data sovereignty, PDPA compliance, and air-gapped security for sensitive workloads in healthcare, finance, and defence.
NovaGenAI implements enterprise-grade security including encrypted data at rest and in transit, air-gapped deployment options, role-based access control, audit logging, model governance dashboards, and compliance with Malaysia's PDPA, GDPR, and sector-specific regulations.
We deploy and fine-tune a wide range of AI models including large language models (LLMs), vision models, speech and voice AI, and domain-specific models for computational biology. Models can be deployed on cloud, on-premise, or hybrid infrastructure depending on security and performance requirements.