Turnkey GPU and NPU compute appliances engineered for secure local inference, continuous model adaptation, and architectural independence. Cloud-grade performance deployed directly inside your enterprise perimeter.
Thermodynamic kernel scheduling and model quantization deliver superior throughput per watt over standard cloud instances.
Zero public network hops or shared multi-tenant throttling, ensuring constant real-time execution speeds.
Completely air-gapped capable; prompts, weights, and results remain strictly bounded inside enterprise hardware.
The Quantum Stack comprises pre-validated, rack-mountable compute nodes equipped with enterprise accelerators. Each appliance ships with factory-optimized runtime environments, automated driver orchestrations, and containerized workload isolation.
As demonstrated in our published whitepaper on autonomous engineering, generalist cloud models hit an "Inference Wall" of latency and VRAM costs in continuous agentic loops. Quantum Tiger optimizes model density to reside within dedicated local memory tiers.
Grounding our architecture in fundamental physics, every operation pays Landauer’s thermodynamic cost (Emin = kT ln 2). By pruning weights and executing sparse event-driven inference, our appliances run significantly cooler within enterprise datacenter power budgets.
Engineered to satisfy India’s Digital Personal Data Protection (DPDP) Act 2023, the infrastructure guarantees that healthcare records, financial ledgers, and government telemetry remain resident within designated domestic premises.
Quantum Tiger provides turnkey on-premise compute nodes and appliances known as the Quantum Stack. These systems integrate high-performance GPU/NPU silicon, optimized low-level CUDA/compiler kernels, and the Corvus operating system for deterministic local inference and model adaptation.
Quantum Tiger achieves a 40% energy efficiency gain through specialized model pruning, thermodynamic-aware architectural scheduling, post-training quantization, and kernel-level pipeline execution that eliminates unnecessary memory swapping and centralized network round-trips.
On-premise enterprise AI infrastructure consists of dedicated physical server appliances installed directly in a company’s own datacenter or server room, allowing internal teams to run LLMs, computer vision, and predictive models completely offline without sending data to third-party cloud APIs.
Regulated enterprises in banking, financial services, healthcare, defence establishments, national labs, and manufacturing companies in India benefit by ensuring absolute compliance with the DPDP Act 2023, guaranteed low-latency uptime, and zero foreign cloud data leakage.