Services
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.
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.
Strategic forecasting and procurement of burstable, on-demand GPU resources to meet fluctuating AI training and inference demands without over-provisioning capital-intensive hardware.
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.
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.
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.
Design and tuning of traditional HPC clusters (CPU-centric with InfiniBand) for massively parallel AI workloads, bridging scientific computing and modern deep learning frameworks.
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.
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.
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.
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.
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.
Upgrade and optimization of legacy on-premises compute clusters for modern AI workloads, including hardware refresh, high-speed networking integration, and software stack containerization.
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.
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.
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.
How We Work
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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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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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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