MLOPS & VLLM • INFRASTRUCTURE

Self-Hosted Llama 3 70B GPU Cluster

vLLM GPU PagedAttention architecture offering 10x token throughput with 100% on-premise GDPR data privacy.

Problem & Cost Context

An enterprise SaaS company was spending $45,000 per month on third-party commercial LLM API calls, while facing strict European Union GDPR requirements that restricted sending proprietary customer data to external cloud AI endpoints.

Solution & GPU Infrastructure

Rohit designed and deployed a **Self-Hosted vLLM Cluster** running open-weight Llama 3 70B across 4x NVIDIA A100 GPUs:

  • PagedAttention Memory Management: Optimized KV-cache memory allocation for 10x higher concurrent request throughput.
  • Dynamic LoRA Adapters: Swapped task-specific LoRA weights on the fly for customer support, code review, and entity extraction.

Verified Client Review

"We were burning over $45k every month on OpenAI API tokens. Rohit designed a self-hosted vLLM GPU cluster on our private cloud that slashed our monthly infrastructure bill by 62% while guaranteeing 100% GDPR data privacy. Rohit is an top-tier MLOps engineer."

Henrik Nielsen

Quantifiable Results

62%
Cost Savings
10x
Throughput
100%
Privacy

Project Specs

Category: Private LLM Infrastructure
Status: Active Production
Architect: Rohit
Tech Stack Used
vLLM CUDA Docker Kubernetes

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