TrayakshAI helps IT professionals, solution architects, enterprises, and technology partners develop expertise in designing, deploying, and optimising end-to-end AI infrastructure.
TrayakshAI helps IT professionals, solution architects, enterprises, and technology partners develop expertise in designing, deploying, and optimising end-to-end AI infrastructure.
Our training programs focus on practical AI infrastructure architecture, GPU computing, AI networking, high-performance storage, AI platforms, and production-ready AI Factory deployments.
Real-world rack topologies, 40kW–100kW+ power delivery, direct-to-chip liquid cooling loops, manifolds, CDUs, and facility PUE optimization.
Hopper H100/H200 and Blackwell B200 architectural internals, FP8/FP4 micro-precision, and NVLink 900GB/s to 1.8TB/s meshes.
Quantum-2 InfiniBand and Spectrum-X RoCE v2, adaptive routing, PFC/ECN flow control, and non-blocking fat trees.
Kubernetes GPU Operator, Slurm workload scheduling, distributed parallel training (Megatron/DeepSpeed), and vLLM serving.
8 production-grade engineering tracks designed by datacenter architects. Built to take infrastructure engineers from silicon topology to multi-node cluster orchestration.
Master multi-rack cluster topologies, power delivery (40kW–100kW+/rack), facility cooling distribution, PUE optimization, and modular POD sizing for mission-critical deployments.
Comprehensive architectural breakdown of NVIDIA DGX and HGX platforms, SXM baseboards, NVSwitch inter-GPU topologies, baseboard management, Redfish automation, and telemetry.
Unlock peak computational throughput across Hopper and Blackwell architectures. Master 4th & 5th Gen Tensor Cores, FP8/FP4 precision, Transformer Engine, and HBM3e bandwidth.
Architect ultra-low latency, lossless networks for massive distributed training. Master Quantum-2 400G & Quantum-X800 InfiniBand, Spectrum-X Ethernet, RoCE v2, and congestion control.
Eliminate I/O bottlenecks and GPU starvation in training pipelines. Configure high-throughput parallel file systems, GPUDirect Storage (GDS), NVMe-oF fabrics, and rapid checkpointing systems.
Deploy and operate mission-critical cluster schedulers. Configure the NVIDIA GPU Operator, Slurm Workload Manager, Kubernetes scheduling plugins, MPI jobs, and multi-tenant quota systems.
End-to-end infrastructure engineering for large language models. Master 3D parallelism (Tensor, Pipeline, Data), Megatron-LM/DeepSpeed training, and high-throughput inference serving with vLLM.
Practical methodologies for workload migration and cloud repatriation. Master NVIDIA DCGM profiling, GPU utilization auditing, power-performance curves, and infrastructure TCO modeling.
Engineered specifically for technical practitioners and leaders responsible for designing, deploying, and operating accelerated compute environments.
Transitioning from legacy x86 virtualization and SAN storage to high-density GPU supercomputing, direct liquid cooling, and 800G fabrics.
Architecting multi-tenant AI platforms, sizing GPU-to-storage bandwidth, and validating end-to-end BOMs before multi-crore capital commitments.
Moving from single-GPU experiments to multi-node distributed training, optimizing NCCL ring collectives, and scaling vLLM inference engines.
Managing facilities retrofitting for 50kW–100kW+ racks, facility water loops, secondary manifolds, and high-efficiency PUE strategies.
Migrating proprietary enterprise workflows on-premises or into sovereign clouds, meeting compliance, privacy, and budget ROI benchmarks.
Building high-performance bare-metal IaaS/PaaS clouds, automating Slurm/K8s cluster provisioning, and multi-tenant GPU monetization.
VARs, OEMs, and consultancy engineering teams upskilling their workforce to deliver turnkey AI Factory deployments for enterprise clientele.
Compare public hyperscaler on-demand GPU pricing against private DGX/HGX AI Factory infrastructure. Discover your break-even horizon and physical facility power footprint.
Registration fees are ₹9,999 only (closing 23 October). Course fees are ₹49,999, which are applicable after registering.
Pay ₹9,999 today to register and lock your seat before the 23 October deadline.
Applicable after registering. Covers complete training, cluster hours & capstone.
First register with ₹9,999 to lock in your cohort seat before 23 October.
Our instructors have architected accelerated compute clusters, high-frequency trading rigs, and hyperscale GPU fabrics.
Directs technological architecture and infrastructure engineering at TrayakshAI. Trayaksh specializes in high-density accelerated computing, liquid-cooled cluster topologies, and ultra-low-latency fabrics for high-frequency trading and enterprise AI workloads.
Spearheads corporate strategy, ecosystem alliances, and enterprise client execution. Geeta oversees commercial operations and strategic hardware partnerships across accelerated computing ecosystems, ensuring rapid transitions from prototypes to mission-critical deployments.
We train on production-grade reference architectures from the industry leaders in accelerated computing, networking, and parallel storage.
Everything you need to know about the cohort curriculum, Razorpay payment options, lab access, and registration.
Complete your registration via Razorpay. Registration deadline closes on 23 October.
Payable today to register
Applicable after registering
Your seat has been reserved in the TrayakshAI AI Infrastructure Cohort.