On-Premise & Private LLM Deployment

Enterprise-grade AI on your own infrastructure — your data never leaves your network

Deploy self-hosted, open-source LLMs (Llama, Mistral, Qwen, GPT-OSS-class 120B models) on your own servers, private cloud, or air-gapped infrastructure — with the retrieval, guardrails, and monitoring stack to run them in production. Proven at national-government scale.

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Key Benefits

Zero data leaves your network — full data sovereignty

No per-token API costs — predictable infrastructure economics

Proven at national-government scale (MoSPI, Govt of India)

Air-gapped deployment capability for classified environments

Your team can operate it — full documentation and training included

Core Technologies

vLLMText Generation InferenceOllamaLlama 3 / Mistral / QwenGPT-OSS 120BQdrantWeaviatepgvector

Deep Dive: On-Prem LLM

For government agencies, defence, BFSI, healthcare, and any enterprise with strict data residency requirements, sending data to OpenAI or Anthropic APIs is a non-starter. We design and deploy fully self-hosted LLM stacks — models, inference servers, vector databases, and application layers — that run entirely within your network perimeter, including air-gapped environments.

This is not theoretical for us. For the Ministry of Statistics and Programme Implementation (Government of India), we deployed a self-hosted 120B-parameter open-source LLM (o3-class reasoning quality) with Qdrant vector search, BGE embeddings, and Docling document processing — serving RAG queries over 10,000+ national statistical documents on secure government infrastructure, with zero external API calls.

We handle the full deployment lifecycle: model selection and benchmarking against your quality bar (Llama 3, Mistral, Qwen, DeepSeek, GPT-OSS 120B), GPU sizing and procurement guidance, quantization for your hardware budget (INT4/INT8, GGUF, AWQ), inference serving with vLLM or TGI for production throughput, and horizontal scaling as usage grows.

Beyond the model itself, production on-prem AI needs the full stack: authentication and role-based access control, prompt/response logging for audit, PII handling, content guardrails, monitoring and alerting, and update pipelines for new model versions. We deliver all of it — Dockerized, documented, and maintainable by your own team.

Key Features & Capabilities

Everything included in our On-Prem LLM service offering.

Model Selection & Benchmarking

Rigorous evaluation of open-source models (Llama, Mistral, Qwen, DeepSeek, GPT-OSS 120B) against your specific tasks, quality bar, and hardware budget before committing.

GPU Infrastructure Design

Right-sized GPU recommendations (A100/H100, L40S, or consumer-grade for smaller models), cluster architecture, and cost projections — buy exactly what you need, no more.

Production Inference Serving

vLLM / Text Generation Inference deployment with continuous batching, quantization (INT4/INT8, AWQ, GGUF), KV-cache optimization, and load balancing for real throughput.

Air-Gapped & Secure Deployment

Fully offline installation for classified or regulated environments — model weights, embeddings, containers, and updates delivered without internet dependency.

Complete RAG Stack On-Prem

Self-hosted vector databases (Qdrant, Weaviate, pgvector), embedding models (BGE, E5), and document processing (Docling) — the full retrieval pipeline inside your network.

Governance, Audit & Monitoring

Role-based access, full prompt/response audit logs, PII redaction, content guardrails, GPU utilization dashboards, and drift monitoring for compliance-grade operation.

Real-World Applications

Use Cases

How organizations across industries are leveraging On-Prem LLM.

Government

Government Document Intelligence

MoSPI (Govt of India) runs our self-hosted 120B LLM + Qdrant RAG stack on secure government infrastructure — 10,000+ statistical documents searchable with zero external API calls.

Financial Services

BFSI Compliance-Safe AI

Banks and insurers deploy on-prem LLMs for document analysis, customer communication drafting, and internal knowledge search without violating RBI/IRDAI data mandates.

Healthcare

Healthcare Records Intelligence

Hospitals run self-hosted models over patient records and clinical documentation, keeping PHI entirely within their own data centre.

Defence / Classified

Defence & Air-Gapped Environments

Fully offline LLM deployments for classified networks — installed, updated, and maintained without any internet connectivity.

What You Get

Deliverables & Outcomes

A complete engagement includes all of the following — no hidden extras, no scope surprises. Our ISO 9001:2015 certified process ensures every deliverable meets documented quality standards.

Model evaluation report with benchmarks on your tasks

GPU infrastructure specification and sizing

Deployed inference stack (vLLM/TGI, quantized models)

Self-hosted RAG pipeline (vector DB, embeddings, ingestion)

Auth, RBAC, and audit logging layer

Monitoring dashboards (latency, throughput, GPU utilization)

Update and model-refresh runbooks

Admin training and full documentation

Technology Stack

Tools & Technologies

Best-in-class tools selected for your specific requirements — balancing performance, cost, and long-term maintainability.

vLLMText Generation InferenceOllamaLlama 3 / Mistral / QwenGPT-OSS 120BQdrantWeaviatepgvectorBGE EmbeddingsDoclingDocker + KubernetesNVIDIA CUDA / Triton

Frequently Asked Questions

Common questions we hear about on-prem llm engagements.

Which open-source LLMs can match GPT-4-class quality on-premise?

For most enterprise tasks, current-generation open models — Llama 3.3 70B, Qwen 2.5 72B, DeepSeek V3, and GPT-OSS-class 120B models — deliver quality comparable to proprietary APIs, especially when paired with a well-built RAG pipeline. We benchmark candidate models on your actual tasks during discovery, so the decision is data-driven rather than hype-driven.

What GPU hardware do we need to run an LLM on-premise?

It depends on model size and concurrency. A quantized 70B model serves a small team on 2× A100 80GB or 4× L40S; a 120B model at government scale typically needs 4–8× A100/H100. Smaller 7–14B models run on a single consumer-grade GPU for lighter workloads. We provide exact sizing and cost projections before you buy anything.

Can this work in a fully air-gapped environment with no internet?

Yes. We deliver model weights, container images, embedding models, and all dependencies as offline installation bundles, and define an update process that works within your security procedures. We have deployed on secure government infrastructure where external API calls are prohibited.

How does on-prem cost compare to using OpenAI or Anthropic APIs?

APIs win at low volume; on-prem wins at scale and on data sovereignty. As a rule of thumb, sustained workloads above a few million tokens per day usually break even on GPU investment within 12–24 months — and for regulated data, on-prem is often the only compliant option regardless of cost. We model your specific economics during scoping.

Who maintains the system after deployment?

Your team, if you want — every deployment includes runbooks, admin training, and full documentation so you're not locked into us. Most clients keep a light support retainer for model upgrades and optimization, but independence is the design goal.

Ready to Deploy On-Prem LLM?

Let's discuss your specific requirements and design a solution that delivers real business outcomes -- not just impressive demos.

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