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  1. EduRankAI
  2. SAMARTH
  3. Capabilities
  4. GPU computing

SAMARTH capability

GPU computing

GPU capacity is expensive to buy and easy to waste. The right answer depends on the workloads, the data, the power available and how the capacity will be shared.

Register for a consultationHow it works

Problems organisations bring

  • Uncertainty between buying, renting or sharing capacity
  • Clusters that sit idle or are always queued
  • Power and cooling limits discovered late
  • Researchers who cannot get access when they need it

Scope of work

  • Workload analysis and capacity sizing
  • Cluster and network architecture
  • Storage and data pipeline design
  • Scheduling and fair-share policies
  • Utilisation monitoring
  • Facility, power and cooling requirements

How an engagement runs

  1. Consultation. Every engagement begins with a registered consultation request and a call with a SAMARTH consultant, who confirms what is actually needed.
  2. Workload study. Measure or estimate the real workloads before sizing anything.
  3. Architecture and options. Owned, colocated, cloud and hybrid options compared on total cost and risk.
  4. Proposal and quotation. Any work beyond the consultation is proposed separately, with its own scope, price, terms and an internally approved quotation.

What is delivered

  • Capacity and workload analysis
  • Reference architecture
  • Procurement specification
  • Operations and scheduling design

Limits we state plainly

Hardware is procured through a separate quoted engagement or by the client directly.

Performance figures are stated as measured or as vendor-specified, and labelled which.

Questions

Do you sell GPUs?

No. SAMARTH helps you decide what to acquire and can coordinate procurement and installation through selected partners under a separate engagement.

Can a university share one cluster across departments?

Yes. Scheduling and fair-share policy design is part of the scope.

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