Infrastructure for serving models, not just training them
Production inference has different requirements from training: concurrency, latency, uptime, power efficiency, scaling rhythm, observability, and predictable operating cost.
AI Inference Infrastructure
AI Inference Infrastructure for Production Workloads
IFT helps enterprises deploy AI inference infrastructure for production model serving, private AI systems, agent workloads, knowledge-base applications, and scalable token-generation capacity.
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Compute, network, storage, power, and cooling together
IFT approaches AI inference infrastructure as a complete system, coordinating GPU servers, cluster networking, storage, rack layout, power distribution, and thermal planning.
Lower TCO through practical deployment design
IFT helps customers balance GPU capacity, rack density, cooling method, site constraints, delivery timeline, and lifecycle operations to reduce total cost of ownership.
Capabilities
Built for procurement, deployment, and operations.
Inference workload assessment
GPU server and cluster architecture design
Rack deployment and infrastructure integration
Network, storage, power, and cooling planning
Base environment setup and acceptance testing
Operations support and performance optimization
LLM inference infrastructure
Private enterprise AI deployment
RAG and knowledge-base systems
AI agent production environments
High-concurrency model serving
What is AI inference infrastructure?
AI inference infrastructure is the compute, network, storage, power, cooling, and software environment used to run trained AI models in production.
How does IFT reduce inference infrastructure risk?
IFT plans the full deployment path from site assessment and hardware selection to rack installation, system integration, testing, and operations support.
Plan your AI infrastructure deployment with IFT.
Share your workload, server quantity, GPU requirements, site conditions, target region, and timeline. IFT will help evaluate the right deployment path.
Contact IFT