How to deploy LLM on-premise — a 2026 guide.

Deploying an LLM on-premise means running a large language model on your own servers — inside your data centre, server room, or co-location facility. No data leaves your network. No cloud API costs. Full control over the model, the data, and the infrastructure.

This guide walks you through every step, from choosing GPU hardware to production monitoring. It is based on our experience deploying private AI for Indian banks, hospitals, and enterprises.

Why Deploy LLM On-Premise

Three reasons Indian businesses deploy LLMs on-premise:

• Data sovereignty. Banking data (RBI regulations), patient records (DPDP Act), and government documents must stay on Indian soil, often within your own network. Cloud AI APIs send data to external servers.
• Cost at scale. At 5,000+ queries per day, on-premise LLMs cost 60-70% less than cloud APIs over 2 years. The breakeven point is typically 3-6 months.
• Customisation. Fine-tune models on your data, add custom guardrails, and control exactly what the model can and cannot say. Cloud APIs offer limited customisation.

Step 1: Choose GPU Hardware

Key hardware specifications: 64GB+ system RAM, NVMe SSD (2TB+), 10GbE networking for multi-GPU setups, redundant power supply, and adequate cooling (plan for 400-3000W heat dissipation).

Buying tip for Indian businesses: Our On-Premise AI Server comes pre-configured with GPU, CUDA, and models installed. Saves 2-3 weeks of setup time.

Step 2: Select Your Model

Not every task needs a 70B model. Smaller models are faster, cheaper, and often sufficient.

• General-purpose chat: Llama 3.1 8B Instruct — fast, accurate, runs on RTX 4090.
• Complex reasoning: Llama 3.1 70B Instruct or Mistral Large — needs A100 GPU.
• Code generation: CodeLlama 34B or DeepSeek Coder.
• Embeddings (for RAG): BGE-large, E5-large, or Nomic-embed.
• Indian languages: Fine-tune Llama on Hindi/Tamil data or use multilingual models.

Our recommendation: Start with Llama 3.1 8B for most use cases. Upgrade to 70B only if accuracy on your specific tasks is insufficient with the 8B model. Test before committing.

Step 3: Set Up the Inference Server

The inference server is the software that runs the model and serves API requests. Top choices:

• vLLM — Best overall performance. PagedAttention for memory efficiency. OpenAI-compatible API out of the box. Our default recommendation.
• Text Generation Inference (TGI) — By Hugging Face. Good for standard deployments. Easy setup.
• Ollama — Easiest setup. Good for testing and small deployments. Not recommended for production at scale.

For production, we recommend vLLM with Docker containerisation, Nginx reverse proxy for SSL termination, and systemd for process management.

Step 4: Build the RAG Pipeline

Retrieval-Augmented Generation lets the LLM answer questions from your company documents accurately.

Components:

• Document ingestion: Parse PDFs, Word docs, and web pages. Chunk into 500-1000 token segments with overlap. Use recursive text splitter.
• Embedding model: Run locally (BGE-large or E5-large) for data privacy. Generate vector embeddings for each chunk.
• Vector database: Qdrant (our recommendation for on-premise) or Weaviate. Store embeddings with metadata.
• Retrieval: Query the vector DB with the user question. Retrieve top 5-10 relevant chunks. Rerank with a cross-encoder for better accuracy.
• Generation: Pass retrieved chunks as context to the LLM. Generate answer with citations to source documents.

RAG accuracy depends heavily on chunk size, embedding quality, and reranking. Expect 85-90% accuracy initially, improving to 93-96% with tuning.

Step 5: Harden Security

• Network isolation: Run the AI server on a private VLAN. No direct internet access. API accessible only from authorised internal IPs.
• Authentication: API key management with rate limiting. RBAC for different user groups (developers vs end-users).
• Encryption: TLS for API traffic. Encrypt model files and vector database at rest.
• Audit logging: Log every API request — user, prompt, response, timestamp. Required for DPDP Act and RBI compliance.
• Input/output guardrails: Filter prompts for injection attacks. Restrict model output to prevent data leakage. Block PII in outputs where required.

Step 6: Set Up Production Monitoring

Monitor these metrics in production:

• GPU utilisation: Should be 60-80% at peak. Below 30% means your hardware is oversized. Above 90% means you need to scale.
• Token throughput: Tokens per second generated. Baseline your models and alert on degradation.
• Latency (P50, P95, P99): Time to first token and total response time. Set SLOs based on user experience requirements.
• Error rate: Failed requests, timeouts, OOM errors. Should be under 0.1%.
• RAG accuracy: Periodic evaluation against ground truth questions. Alert if accuracy drops below threshold.

Use Prometheus + Grafana for metrics. vLLM exposes Prometheus metrics natively.

Common Mistakes to Avoid

• Choosing too large a model. A 70B model on insufficient hardware = slow, expensive, and frustrating. Start small, benchmark, and upgrade only if needed.
• Skipping RAG evaluation. Deploy RAG without testing accuracy on real questions. Build an evaluation set of 50-100 question-answer pairs and test before going live.
• No monitoring. Deploy and forget. Models degrade, GPU memory fills, and latency creeps up. Monitor from day one.
• Ignoring security. Exposing the LLM API without authentication or guardrails. One prompt injection can extract training data or bypass restrictions.
• No update plan. New models release every month. Have a process for evaluating, testing, and deploying model updates.