Shakti AI Lab

Shakti AI Lab provides flexible, scalable access to state-of-the-art GPUs, eliminating upfront investments and maintenance costs. Its no-code setup streamlines deployment, making it far more cost-effective and adaptable than traditional physical labs. As business and research calls for you can scale the compute on Shakti AI Lab, whereas physical infrastructure suffers from long procurement and deployment cycles that slow down innovation. With these physical setups, you either continue using older GPUs you started with or manage separate clusters for newer hardware, creating complexity for administrators and operational overhead.

Shakti AI Lab provides NVIDIA L40S and H100 GPUs, accelerating AI research, prototyping, and experimentation while delivering high efficiency and performance.

You can link external storage solutions directly to the lab for access to your datasets. This eliminates the need for data migration. Alternatively, you can move a copy of your data to the Yotta data center. This places your data closer to the compute resources, reducing latency.

The lab supports diverse projects including training large language models, computer vision, NLP, and other AI/ML tasks, leveraging powerful GPUs like the L40S and H100.

Yes. Pilot programs are available on a case-by-case basis, enabling you to test AI Lab's features before making long-term commitments.

AI Lab requires no on-premises hardware; a stable internet connection and basic devices are sufficient as heavy computing tasks are handled in the cloud.

Shakti AI Lab is designed specifically for academia with features like tailored AI training programs, admin-controlled environments, student-centric pre-configured container images, and transparent pricing with no hidden costs.

Shakti AI Lab provides flexible investment plans, allowing you to start small and scale as needed. Its per-workstation pricing, intuitive setup, and predictable costs help you manage budgets efficiently.

The platform empowers administrators with tools to manage and monitor lab usage, allocate resources, and customize environments for educational or research needs, ensuring efficient resource utilization.

Users can add or reduce workstations based on monthly or annual basis for situations such as hackathons or short-duration lab sessions. This ensures that computational power is available when needed, unlike physical labs which are constrained by space and infrastructure.

The platform offers ready-to-use GPU profiles and pre-configured container images, eliminating complex setup processes. You can also configure custom container images as needed for different courses and research projects.

Shakti AI Lab supports seamless integration with external storage solutions, enabling quick access to source code, proprietary data, and training datasets via no-code connections.

Shakti AI Lab offers fixed, predictable pricing without hidden fees for data ingress or egress. This transparency helps you manage lab budget effectively without unexpected expenses.

Shakti AI Lab exclusively offers access to the latest Nvidia GPUs, such as H100 and L40S, ensuring you work with cutting-edge hardware for AI research and development.

Shakti AI Lab provides a scheduler that allows lab administrators to pre-configure lab access for end-users by grouping them into batches. Administrators can also pre-configure the GPU profile and container images for each batch.

Shakti AI Lab provides Nvidia's Multi-Instance GPU (MIG) slicing technology. MIG allows a single physical GPU to be partitioned into up to seven smaller, isolated instances, each with its own dedicated resources, such as memory, compute cores, and cache. This enables workload-level isolation, ensuring that one user's workload won't interfere with another's and helps avoid the "noisy neighbor" problem that frequently occurs in time slicing environments. MIG is ideal for cloud environments where multiple users need guaranteed resources and consistent performance.