Use Case: Text analytics, small fine-tuning, inference workloads.
2. Neural AI / Deep Learning:
2 × RTX 6000 Ada or 1 × A100, 256–512GB RAM, 10TB NVMe.
Use Case: Robotics, NLP, scientific model training.
3. Visual AI / Computer Vision:
2 × RTX 4090 / A10, 256GB RAM, 20TB NVMe.
Use Case: Object detection, video analytics, quality inspection.
4. Audio & Speech AI:
1 × A10 / RTX 6000, 128GB RAM, 8TB NVMe.
Use Case: Real-time ASR, voice synthesis, language training.
5. Robotics & Embedded AI:
Jetson Orin / Coral TPUs for edge inference.
Dedicated staging benches for robotics R&D.
Supporting Infrastructure:
25 Gbps network backbone, redundant power and cooling.
Web-accessible dashboards for model control and monitoring.
Hybrid cloud expansion for burst workloads.
Positioning
Dikshak’s AI infrastructure enables clients to build, train, and deploy models securely within India — balancing performance, sustainability, and scalability.