GPU offers, without pretending IMRTechnologies owns every GPU rack.
IMRTechnologies can provide GPU-backed infrastructure through approved partner supply and manage onboarding, workload sizing, support, and business integration. That is the honest and scalable model.
Positioned for supported workloads, not fantasy inventory
Public pricing is anchor pricing. Final deployment depends on region, stock, workload pattern, and support requirements.
GPU Dev
From ₹6,999/mo
For inference, prototyping, smaller fine-tunes, and remote AI work.
- T4/L4 class starting point
- Partner-backed compute
- IMRTechnologies-managed onboarding
- Best for early-stage AI teams
GPU Workstation
From ₹14,999/mo
For rendering, visual workloads, model experimentation, and power users.
- A16 / equivalent class
- Remote workstation pattern
- Managed delivery
- Can support media and graphics stacks
GPU Training Node
From ₹24,999/mo
For sustained AI training, HPC-style jobs, and larger memory needs.
- V100/A100 class quote path
- Architecture review before activation
- Cost and usage planning
- Monitoring and support
Dedicated GPU / Cluster
Custom quote
For high-end or long-running workloads that need sustained supply.
- Dedicated cluster design
- Partner-backed sourcing
- Commercial review
- Compliance-sensitive workload screening
AI inference and agents
Inference APIs, copilots, agent backends, and document AI pipelines.
Fine-tuning and training
Model fine-tuning, research, experimentation, and training jobs.
Rendering and media workloads
Graphics, transcoding-adjacent compute, and workstation-style GPU needs.
Enterprise custom compute
HPC, analytics, or custom business workloads needing larger GPU capacity.
Important fit note for IPTV, media, and streaming customers
IMRTechnologies can service streaming-adjacent customers, including middleware, panels, app backends, analytics, remote workstations, and qualified GPU or high-throughput workloads. But we do not auto-promise unrestricted media delivery or rights-sensitive streaming without workload and compliance review.
Workload sizing first
We need clarity on concurrency, storage, bitrate, transcoding, and region.
Compliance review
Rights-sensitive or risky streaming use cases need review before quote or provisioning.
Best-fit infrastructure
We can place the workload on IMRTechnologies-managed or partner-backed compute depending on the profile.
Request GPU / AI Compute Review
Use this for inference, fine-tuning, rendering, remote GPU workstations, or AI workloads that need IMRTechnologies-managed delivery.