The On-Prem AI Library
On-prem AI, explained by the people who deploy it for a living.
Everything about running AI inside your own infrastructure: which model, what hardware, what it costs, how it stays compliant, and what to build first. No email walls, no vendor fog. NetRay fine-tunes models, hosts them inside your walls, and builds the applications on top. This library is the thinking behind that work.
Self-Hosted LLMSelf-hosted LLMs, done like production software.Deploy a self-hosted LLM inside your own infrastructure. NetRay handles model selection, GPU sizing, vLLM serving, fine-tuning, and day-2 operations. Zero egress, you own the weights.Read the guidePrivate AI AssistantThe assistant your team already uses, minus the data leaving.Give your team a ChatGPT-class assistant that runs entirely inside your network. NetRay deploys private AI assistants with your documents, your permissions, and zero data egress.Read the guideAir-Gapped AIAI that works where the internet doesn't exist.Deploy and operate LLMs in fully air-gapped environments. Offline model registries, sneakernet updates, and drift monitoring for classified, ITAR, and critical-infrastructure networks.Read the guideFine-Tuning ServicesMake the model yours. Literally.Fine-tune open-weight LLMs on your company data: LoRA/QLoRA, full SFT, DPO, distillation, and quantization. Trained on your infrastructure, evaluated on your tasks. You own the weights.Read the guidePrivate RAGChat with thirty years of documents. Inside your walls.Production RAG over your private documents, deployed inside your network. Permission-aware retrieval, source citations, and answers grounded in your files. Nothing leaves your perimeter.Read the guideCost & SizingThe on-prem AI math, with the parts vendors skip.What on-prem AI actually costs: GPU sizing by model class, self-hosting vs API break-even math, and the line items teams forget. Real numbers for planning your deployment.Read the guideDeployment DecisionOn-prem vs cloud AI: decide it with data grades, not opinions.On-premise vs cloud AI, decided by evidence: data classification, workload shape, cost curves, and compliance. The framework NetRay uses with regulated manufacturers, plus when hybrid wins.Read the guideRegulated IndustriesThe most valuable data is the data AI vendors can't touch.Deploy AI where compliance is non-negotiable: aerospace, defense, and regulated manufacturing. On-prem and air-gapped LLMs aligned with ITAR, CMMC, and DFARS obligations.Read the guideOpen-Weight ModelsOpen weights turned the best AI into something you can own.Put open-weight models (Llama, Mistral, Qwen class) into enterprise production: licensing review, benchmarking on your tasks, fine-tuning, and serving on your infrastructure.Read the guideReadiness AssessmentBefore you buy a single GPU: what you have, what it's graded, what to build first.Before you buy a single GPU: NetRay's AI readiness assessment grades your data, inventories your infrastructure, ranks your use cases by ROI, and hands you a build roadmap.Read the guideCoding AssistantCompletion, review, and refactoring on a model that signed your NDA by physically living in your rack.Give developers AI code completion, review, and refactoring on a model that never sees the internet. Self-hosted coding assistants with IDE integration, tuned to your codebase.Read the guideAI InfrastructureThe rack that replaces the API bill.Design and stand up on-prem AI infrastructure: GPU server selection, inference serving with vLLM/TensorRT-LLM, networking, storage, and the MLOps layer that keeps it healthy.Read the guideSmall Language ModelsThe right model is usually smaller than the demo.Small language models (1B-15B) fine-tuned to one job often beat giant models on enterprise tasks, at a fraction of the cost and latency. When SLMs win, and how NetRay deploys them.Read the guideAI AgentsAgents that act inside your perimeter, on a leash you hold.Deploy AI agents that act, not just answer, inside your network: tool allowlists, approval gates, and full flight recording. Agentic automation for ERP, documents, and operations.Read the guide
Want the deep implementation detail? Read the full on-premise AI playbook, compare with cloud and hybrid architectures, or start with AI consulting.