Services
AI Supercomputing and Hybrid Cloud Architecture
AI Supercomputing and Hybrid Cloud Architecture
Integration of CPUs, GPUs, ASICs, and alternative computing paradigms for complex training workloads with model-aware compute, hybrid operating patterns, and intelligent edge tooling. Sub-services include GPU-as-a-Service capacity planning, FinOps consulting for AI cloud consumption, hybrid cloud architecture for deep learning, and enterprise integration of NVIDIA DGX infrastructure.
Hybrid Cloud AI Architecture Consulting
Design of secure, high-performance AI infrastructure that seamlessly spans on-premises data centers and multiple public clouds, optimizing for data gravity, latency, and cost while avoiding vendor lock-in.
GPU-as-a-Service Capacity Planning
Strategic forecasting and procurement of burstable, on-demand GPU resources to meet fluctuating AI training and inference demands without over-provisioning capital-intensive hardware.
AI Compute FinOps and Cost Optimization
Implementation of financial operations (FinOps) frameworks and tooling to monitor, analyze, and optimize cloud and on-premises AI compute spend, achieving 30-50% cost reductions through intelligent resource management.
Enterprise DGX Infrastructure Integration
End-to-end deployment and integration of NVIDIA DGX SuperPOD and BasePOD systems into existing enterprise data centers, including networking, storage, and management software for turnkey AI supercomputing.
Multi-Cloud AI Workload Orchestration
Engineering of unified orchestration platforms using Kubernetes (K8s) and tools like KubeFlow to dynamically schedule and manage AI training jobs across AWS, Azure, and GCP based on resource availability and cost.
High-Performance Computing for AI
Design and tuning of traditional HPC clusters (CPU-centric with InfiniBand) for massively parallel AI workloads, bridging scientific computing and modern deep learning frameworks.
Large-Scale Model Training Infrastructure
Architecture of dedicated, fault-tolerant clusters optimized for training foundation models and LLMs with thousands of GPUs, incorporating advanced parallelism strategies (data, model, pipeline) and checkpointing.
AI Infrastructure Resilience and Scalability
Design of highly available and elastically scalable AI platforms with automated failover, disaster recovery plans, and the ability to seamlessly scale from pilot to production workloads.
AI Infrastructure as Code Implementation
Codification of AI infrastructure using Terraform, Ansible, and Pulumi for reproducible, version-controlled, and automated provisioning of GPU clusters, storage, and networking across hybrid environments.
AI Workload Performance Benchmarking
Rigorous testing and analysis of AI training and inference jobs across different hardware (GPUs, ASICs) and cloud configurations to identify bottlenecks and establish performance baselines for SLAs.
Elastic AI Compute Platform Architecture
Design of auto-scaling platforms that dynamically provision and deprovision GPU/CPU resources in response to real-time AI workload queues, maximizing utilization and minimizing idle time.
On-Premises AI Cluster Modernization
Upgrade and optimization of legacy on-premises compute clusters for modern AI workloads, including hardware refresh, high-speed networking integration, and software stack containerization.
Hyper-Scale AI Model Deployment Infrastructure
Engineering of low-latency, high-throughput serving platforms for deploying massive models (100B+ parameters) to global user bases, incorporating model quantization, continuous batching, and advanced load balancing.
AI Infrastructure Security Architecture
Implementation of defense-in-depth security for AI supercomputing, covering network segmentation, identity and access management (IAM) for GPU resources, and secure data pipelines to protect sensitive training data.
Sustainable AI Supercomputing Design
Architecture of energy-efficient AI compute facilities focusing on power usage effectiveness (PUE), liquid cooling integration, and workload scheduling to minimize carbon footprint and operational costs.
Partnered with leading AI, data, and software stack.
How We Work
Custom AI workflows for your Business
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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Review the use case
We understand the task, the users, and where AI can actually help.
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Pick the right approach
We define what needs search, automation, or product integration.
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Build the first useful version
We implement the part that proves the value first.
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Improve from there
We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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