AI Infrastructure & Compute Partner:  


Predictable GPU Capacity for AI Teams That Cannot Afford Delays


GPU scarcity is no longer a temporary inconvenience — it is a structural constraint.


AI teams worldwide face blocked quotas, long waitlists, unstable spot capacity, and slow procurement cycles that directly impact training timelines and production deployments.


Intravya exists to remove that uncertainty.


We provide predictable, production-grade GPU infrastructure for AI, HPC, and advanced compute workloads — structured around real deployment needs, not best-effort availability.


We are not a cloud provider.

We are not a marketplace.

We are a capacity partner.





What We Do


Intravya provides on-demand and reserved access to enterprise-grade GPU infrastructure through a global network of production-ready data center partners.


We enable organizations to:


Train and fine-tune large-scale AI and GenAI models


Run sustained, high-throughput inference workloads


Execute HPC, simulation, and research workloads


Scale AI programs without owning or operating hardware



Capacity is aligned to confirmed demand and deployment timelines, ensuring reliability from pilot to production.





GPU Infrastructure Capabilities


Enterprise GPU Architectures


We support modern GPU platforms optimized for real-world AI workloads:


NVIDIA H100 / A100:

Foundation models, LLM training, large-scale fine-tuning


NVIDIA L40 / L40S:

Production inference, multimodal AI, computer vision


AMD MI300 Series:

High-performance AI and HPC compute


All capacity is provisioned to meet throughput, isolation, and reliability requirements, not theoretical benchmarks.





Deployment Models Built for Execution


We structure GPU access the way serious teams operate — with clarity, control, and flexibility.


Dedicated GPU Allocation:

Reserved, single-tenant capacity for ongoing and mission-critical workloads


On-Demand Compute:

Flexible access for burst demand and variable workloads


Pilot Allocations:

Controlled environments for benchmarking, validation, and proof-of-concept


Long-Term Capacity Commitments:

Stable GPU access aligned to scaled AI programs



Clients retain the ability to scale without vendor lock-in or forced ecosystem dependencies.





Infrastructure & Data Center Standards


All infrastructure is deployed through enterprise-grade data center partners, selected for operational maturity — not experimentation.


Tier III / Tier IV aligned facilities


High-density GPU-ready power and cooling


Low-latency, high-throughput network architecture


Enterprise security and compliance readiness



Every deployment is engineered for production reliability, not temporary availability.





Who We Work With


Intravya works with organizations where infrastructure failure is not an option:


AI and GenAI companies training proprietary models


Enterprises deploying internal AI platforms at scale


Research labs and applied AI institutions


AI SaaS providers and ISVs


System integrators and solution providers



If your roadmap depends on consistent GPU throughput, we align capacity to your execution plan.





How We Are Different


Most providers sell access.

We deliver assurance.


Confirmed GPU allocation in a constrained global market


Dedicated and reserved capacity — not interruptible spot compute


Capacity planning led by humans, not ticket queues


Transparent commercial structures


No forced cloud lock-in



We operate as a long-term compute partner, not a transactional reseller.





Why Intravya


Zero CapEx exposure


Predictable GPU availability when timelines matter


Stable pricing and allocation windows


Global data center partner ecosystem


Strategic infrastructure alignment, not just provisioning



Our role is simple:

Ensure infrastructure is never the reason your AI program slows down.





Engage With Us


If you are planning an upcoming training cycle, scaling inference, or facing GPU availability constraints, we can provide:


Confirmed capacity options


Deployment timelines


Allocation and commitment structures



To begin, share:


Workload type (training / inference / HPC)


Preferred GPU architecture


Target deployment timeline


Expected scale



Our team responds with specific, actionable options — not generic pricing sheets.

Contact Info

E-mail ID: Info@intravya.com