Service Overview & Business Impact

Eliminate escalating monthly cloud API costs and data privacy concerns. We design, deploy, and optimize self-hosted open-weight LLM inference clusters on private cloud or on-premise hardware.

vLLMCUDAKubernetesDockerNVIDIA A100/H100Ray

4-Layer Engineering Architecture

Layer 1: Hardware Provisioning & Topology

Configures multi-GPU nodes with NVLink interconnects and optimized Tensor Parallelism.

Layer 2: vLLM PagedAttention Server

Deploys vLLM with PagedAttention memory management, eliminating KV-cache memory fragmentation.

Layer 3: Prefix Caching & Dynamic Batching

Enables automatic prompt prefix caching for 70% lower first-token latency on repeating system prompts.

Layer 4: Kubernetes Ingress & Auto-Scaling

Wraps inference endpoints in OpenAI-compatible API servers behind Kubernetes ingress controllers.

Implementation Roadmap & Deliverables

Phase 1: GPU Sizing & Benchmark Audit

Evaluate token throughput requirements and select optimal GPU hardware configurations.

Phase 2: vLLM Cluster Setup

Install CUDA drivers, vLLM engine, and model quantization (AWQ/GPTQ) pipelines.

Phase 3: Security & Egress Lockdown

Enforce 100% private data isolation and air-gapped network policies.

Phase 4: Production Load Stress Testing

Validate token throughput under 500+ concurrent user requests.

Ready to Deploy This AI Architecture?

Book a 1-on-1 technical scoping session directly with AI & Data Science Consultant Rohit.

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Consultant Profile

Rohit

AI & Data Science Consultant

2+ Decades AI Experience

Building neural networks since 2004 at IIT Roorkee (mentored by Dr. Sunil Padhi, HOD Electrical Dept) and Unix CDR automation scripts at Xalted Bengaluru in 2007 (mentored by Srinivas Sir). Specializing in Agentic AI, Enterprise RAG, and MLOps.

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vLLM & Private GPU Cluster Engineering