GPU clusters, LLM inference pipelines, Kubernetes AI platforms, and multi-cloud AI deployment — we architect the compute backbone that keeps your AI running fast, cheap, and reliably.
45%
GPU cost reduction
10x
Training throughput
99.95%
Inference uptime
Infrastructure Health Dashboard
End-to-end design and management of training compute — from A100/H100 cluster orchestration to distributed training with NCCL and DeepSpeed.
Maximize throughput and minimize latency for LLM serving using quantization, speculative decoding, and continuous batching on vLLM and Triton.
Architect and deploy AI systems across AWS, Azure, GCP, and specialized GPU clouds — with unified observability and cost management.
Production-grade ML serving on Kubernetes — KServe, auto-scaling, canary deployments, and GPU resource quotas for multi-tenant AI platforms.
Reduce GPU bills by 45% through spot instance orchestration, model batching, auto-scaling policies, and right-sizing compute to actual workload patterns.
Zero-trust networking, data encryption in transit and at rest, VPC isolation, IAM hardening, and compliance-ready AI infrastructure for regulated industries.
We're platform-agnostic — selecting and managing the right compute for each workload.
SageMaker, Bedrock, EC2 P4/P5, EKS, S3, Glue
Azure ML, OpenAI Service, AKS, NDv4/NDv5, Data Factory
Vertex AI, TPUs, GKE, BigQuery ML, Dataflow
H100/A100 bare metal, Kubernetes, InfiniBand networking
H100 clusters, reserved GPU, training compute
OpenStack, VMware, on-premise NVIDIA DGX, bare metal
It varies widely by model size, traffic, and latency requirements. A Llama 3 70B model serving 1,000 requests/day on managed cloud GPU can cost $200-500/month. We help clients reduce inference costs by 40-60% through model quantization, batching optimization, and right-sizing GPU instances. We provide detailed cost modeling before any deployment.
Yes. We design and deploy AI infrastructure on private cloud (VMware, OpenStack), on-premise GPU servers, and hybrid environments. For regulated industries requiring data sovereignty, we have extensive experience deploying complete LLM stacks within customer-controlled infrastructure.
Our standard architecture includes multi-region active-active deployment, Kubernetes pod autoscaling, load balancing with health checks, automatic failover, and graceful degradation. We consistently achieve 99.9%+ uptime SLAs for mission-critical inference endpoints.
We'll audit your current AI infra spend and identify savings opportunities within 2 weeks — at no cost.
Request Free Infra Audit