The gap we fill
Your AI infrastructure partner — not a hardware reseller
Running AI on your own infrastructure is no longer reserved for hyperscalers. Open-weight models now rival proprietary ones, and modern GPU servers put serious training and inference capability within reach of a single rack. What remains hard is everything around the hardware: sizing it correctly, getting it delivered and powered in India, standing up the software stack, and keeping it running.
That is the gap VitalMed fills. We act on your behalf across the entire lifecycle — vendor-neutral in hardware selection, hands-on in deployment, and accountable for the result: infrastructure your students or developers actually use.
Process
Full lifecycle, one partner
Four phases from first conversation to production workloads
Assess & size
We start with your workloads — training, fine-tuning, inference, or simulation — and translate them into concrete hardware requirements: GPU memory, compute, storage, and networking. You get a sizing report and TCO analysis before any purchase decision.
Procure
As a vendor-neutral partner, we evaluate server platforms and accelerator options against your requirements — not a sales quota. We manage configuration validation, import logistics into India, GST treatment, and warranty coverage.
Deploy & integrate
Rack installation, power and cooling provisioning, high-speed networking, cluster configuration, and the full software stack — from drivers and schedulers to model-serving frameworks. We benchmark against your workloads before handover.
Operate & support
Ongoing monitoring, utilization reporting, model and framework updates, capacity planning, and SLA-backed support — so your team uses the infrastructure instead of babysitting it.
Who we build for
Two very different deployments, one disciplined approach
Educational institutions
Campus AI labs and research clusters
- check_circleOn-campus GPU infrastructure for AI & Data Science programs
- check_circleStudent environments for fine-tuning and training open models
- check_circleDual-purpose systems that also serve engineering simulation and CAE workloads
- check_circleCurriculum integration support and faculty enablement
- check_circleMulti-user scheduling so one investment serves every department
Enterprises
Self-hosted AI for privacy and control
- check_circleOn-premise serving of open-weight language and coding models
- check_circleAI coding assistants for development teams — high concurrency, long context
- check_circleComplete data privacy: code and prompts stay inside your network
- check_circlePredictable costs compared to per-token cloud API pricing
- check_circleSized for your team today, with a clear path to scale
Capabilities
What we handle
Every layer between an empty room and a running model
GPU capacity planning
Right-sizing accelerator memory and compute for the models you actually run — from compact open models to large mixture-of-experts deployments.
Model serving & inference
Production inference stacks for open-weight language models with high concurrency, long context, and multi-user serving.
Power & cooling engineering
Site assessment, electrical provisioning, and thermal planning so dense GPU systems run reliably in Indian facility conditions.
High-speed networking
Cluster interconnects, storage networking, and campus or office network integration for multi-node and multi-user access.
Security & data sovereignty
Your models, prompts, and data never leave your premises. Access control, isolation, and audit trails built in from day one.
Multi-user environments
Scheduling, quotas, and workspace isolation so students, researchers, or development teams share one system without conflicts.
Benchmarking & validation
We validate performance against your real workloads — tokens per second, batch throughput, training time — before and after deployment.
Managed operations
Proactive monitoring, updates, and rapid response support from a team that deployed the system and knows it end to end.
The case for owning it
Why on-premise AI
Cloud APIs are the fastest way to start. Owning your infrastructure is often the smartest way to scale.
Data sovereignty
Sensitive code, research, and business data stay on hardware you own, inside your network.
Predictable economics
A one-time investment with known running costs — no per-token billing that grows with adoption.
Low latency
Inference on your local network, unaffected by internet routes or third-party rate limits.
Full control
Choose your models, your fine-tunes, and your upgrade schedule — nothing deprecated out from under you.