On-premise LLM deployment means running a large language model on hardware you control, whether that is your own data center, a colocation rack in India or a dedicated private cloud, so prompts, documents and outputs never cross your network boundary. For banks, insurers, hospitals and government suppliers, it is often the cleanest way to use generative models with regulated data.
If you already know which workloads must stay inside your network, describe them through our project form and we will reply with a sizing approach rather than a sales deck.
Why Indian firms are bringing LLMs in-house
Hosted model APIs are convenient, but they put a third party in the data path. Several pressures push Indian organizations toward private deployments:
- The DPDP Act, 2023 and its rules make you accountable as a data fiduciary for every processor that touches personal data, including model providers and their sub-processors.
- Sector expectations from regulators such as RBI, SEBI and IRDAI around outsourcing, data storage and audit access are easier to meet when the whole stack sits under your own controls.
- Customer contracts increasingly forbid sending client documents to external AI services at all.
- Intellectual property such as source code, drawings and pricing logic is something many leadership teams simply will not share outside.
When a hosted API is still the right answer
Not every use case needs a private model. Marketing copy drafts, public FAQ assistants and internal experiments on synthetic data can usually run on a hosted API with a sound data processing agreement. A sensible plan routes only sensitive workloads to the private stack and keeps the rest simple.
Choosing a model you can run and legally use
On-premise means open-weight models. The main families to shortlist today are Meta's Llama, Mistral, Alibaba's Qwen, Google's Gemma and DeepSeek. Each comes with its own license, and the terms differ: some are Apache 2.0, while others carry acceptable-use policies or conditions that legal should read before anything reaches production.
Size the model to the task, not the leaderboard
A small or mid-sized model paired with good retrieval often matches a much larger one on extraction, classification and grounded question answering, at a fraction of the hardware. Larger models earn their place on multi-step reasoning, long documents and complex code. The only reliable way to decide is to score shortlisted models on an evaluation set built from your own documents and questions.
Test Indian language coverage early
If users will ask in Hindi, Kannada, Tamil or mixed Hinglish, include those prompts in the evaluation set from day one. Tokenizer efficiency and answer quality vary widely across Indic scripts, and a model that looks strong in English can disappoint here.
GPU sizing factors for on-premise LLM deployment
Hardware is usually the longest lead-time item, so sizing deserves care. The numbers that matter:
- Parameters and precision. At 16-bit precision, weights need roughly two bytes per parameter; 8-bit roughly halves that and 4-bit halves it again.
- KV cache. Memory for attention state grows with context length multiplied by the number of concurrent requests. Long-context RAG with many users can need more memory for cache than for weights.
- Latency targets. Time to first token and tokens per second per user decide whether you need tensor parallelism across several GPUs.
- Supporting models. Embedding models, rerankers and speech or OCR models also need GPU or CPU capacity.
- Redundancy. Plan for at least one spare node or degraded-mode behavior when a card fails.
Quantization trades quality for capacity
AWQ, GPTQ and FP8 formats let a model fit on fewer GPUs and serve more users, but quality can slip on edge cases. Run the same evaluation set against the quantized and full-precision builds before you commit hardware.
The serving stack, layer by layer
Inference server
vLLM is the common default thanks to paged attention and continuous batching; SGLang and NVIDIA TensorRT-LLM with Triton are strong alternatives when you want maximum throughput on NVIDIA hardware. Ollama and llama.cpp are excellent for laptops, workstations and edge boxes, but they are a weaker fit for serving many concurrent production users.
Retrieval and orchestration
Most private deployments are really retrieval systems: an embedding model, a vector store such as pgvector, Qdrant or Milvus, and permission-aware retrieval so users only see answers drawn from documents they are allowed to read. Our guide to building production RAG systems covers this layer in depth.
Gateway and observability
Put an OpenAI-compatible gateway in front of the model so applications never talk to GPUs directly. It handles authentication, rate limits, prompt logging with redaction and routing between models. GPU health, queue depth and latency belong on the same dashboards as the rest of your infrastructure.
This is the kind of build iJurug Soft takes on within its AI and cloud services: senior engineers on every engagement, fixed milestones from discovery to launch, and long-term support after go-live. Our checklist for vetting an LLM development partner lists the questions worth asking any team, including ours.
Security, access control and updates
- Isolation. Keep inference nodes on a segmented network; fully air-gapped setups are possible if you plan model and package transfers carefully.
- Artifact provenance. Download weights from the publisher, verify checksums and prefer safetensors files over formats that can execute code on load.
- Prompt injection. Treat retrieved documents as untrusted input, restrict what tools the model can call and require human approval for consequential actions.
- Audit trail. Log who asked what, which sources were used and what was returned, with retention aligned to your policies.
Treat model upgrades like software releases
New open-weight models arrive constantly, and drivers, CUDA versions and inference servers change underneath them. Every upgrade should pass the evaluation set, run in staging and ship with a rollback path to the previous model.
Frequently asked questions
How long does an on-premise rollout take?
It depends mostly on hardware. If suitable GPUs already exist, a pilot on one use case can move quickly; if hardware must be procured, delivery lead time usually sets the schedule. Discovery and evaluation can run in parallel with procurement.
Can we start in the cloud and move on-premise later?
Yes, if you build on an open-weight model and an OpenAI-compatible API from the start. The same containers can move from a private cloud tenancy to your racks with configuration changes rather than a rewrite.
Do we need to fine-tune the model?
Usually not at first. Retrieval solves most knowledge problems; fine-tuning helps with format, tone or narrow tasks. See our take on fine-tuning versus RAG for when each makes sense.
What will the hardware and build involve?
Scope drives it: model size, concurrency, latency targets, compliance requirements and the number of integrations. We do not publish rates; we size the setup from your workload and quote against it.
Ready to keep your AI inside your own walls? Share your use cases, data sensitivity and existing infrastructure through our contact form, or write to info@ijurugsoft.com. You will hear back from an engineer, not a salesperson, with a proposed deployment path, a hardware sizing approach and a milestone plan.