On-Premise LLM Infrastructure: GPU, RAM & Storage Requirements

Deploying large language models (LLMs) on your own infrastructure is increasingly viable and often necessary for Indian enterprises dealing with sensitive data, strict regulatory requirements, or high query volumes. On-premise deployments give you control over data, cost, latency, and IP in a way that public cloud APIs often cannot.

Key Drivers for On-Premise

• Data sovereignty & compliance

Indian regulations like RBI data localization and the DPDP Act require certain categories of sensitive and financial data to remain within India. On-premise deployments (or tightly controlled private data centers within India) ensure:

• Data never leaves your controlled network
• Easier compliance audits and documentation
• No ambiguity about cross-border data flows
• Cost control at scale

For enterprises handling 50,000+ queries/day, on-premise inference typically becomes 60–80% cheaper than cloud LLM APIs over a 12–24 month horizon. You pay upfront for hardware, but:

• Per-query cost drops sharply as usage grows
• You avoid unpredictable API price changes
• You can amortize GPU investments across multiple internal AI workloads
• Low latency for interactive applications

Running inference locally avoids wide-area network hops to global cloud regions:

• On-premise: ~50–200 ms response times for many workloads
• Cloud APIs: ~500–2000 ms typical end-to-end latency

This matters for chatbots, agentic workflows, and real-time decision systems.

• Uptime independence

With on-premise, you are not exposed to:

• Third-party API outages
• Rate limits and throttling during peak usage
• Sudden policy or pricing changes
• IP and prompt protection

Your proprietary:

• Prompts and system instructions
• Fine-tuned models
• RAG pipelines and business logic

stay entirely within your network, reducing IP leakage risk and simplifying legal review.

When Cloud APIs Make More Sense

On-premise is not always the right answer. Cloud APIs are often better when:

• Low volume usage

If you are under 1,000 queries/day, the economics usually favor APIs. Hardware purchase and MLOps overhead will not pay back quickly.

• Need the latest frontier models immediately

If you must use the newest GPT-4 class models on day one of release, cloud APIs are faster to adopt than waiting for on-prem weights or compatible open models.

• No internal ML / MLOps capability

On-prem requires at least a small team that can:

• Manage GPUs, drivers, and CUDA
• Deploy and monitor inference servers
• Handle upgrades, security, and observability

If you are in early experimentation or POC phase, starting with cloud APIs and then moving to on-premise as usage stabilizes is often the most pragmatic path.

GPU Requirements and Selection

The GPU is the single most important component for LLM inference. Correct sizing can save lakhs in capex and opex.

GPU Sizing by Model Size

7B Parameter Models (e.g., Llama 3 8B, Mistral 7B)

• Minimum: NVIDIA A10 (24GB VRAM) with 4-bit quantization
• Recommended: NVIDIA A100 (40GB) for full precision or higher concurrency
• Concurrent capacity: ~10–50 simultaneous requests per GPU (depending on context length and throughput targets)
• Indicative cost in India:
• A10: ₹3–4 lakh
• A100 40GB: ₹8–10 lakh

13B Parameter Models (e.g., Llama 2 13B, CodeLlama 13B)

• Minimum: NVIDIA A100 (40GB) with 4-bit quantization
• Recommended: NVIDIA A100 (80GB) for production workloads
• Concurrent capacity: ~5–25 simultaneous requests per GPU
• Indicative cost in India:
• A100 80GB: ₹10–14 lakh

70B Parameter Models (e.g., Llama 3 70B, Qwen 72B)

• Minimum: 2× A100 (80GB) with 4-bit quantization using tensor parallelism
• Recommended: 4× A100 (80GB) for production with headroom and better throughput
• Concurrent capacity: ~5–15 simultaneous requests per GPU pair
• Indicative cost in India:
• 4× A100 80GB setup: ₹45–60 lakh (including server chassis and supporting components)

Alternative: NVIDIA H100

• 2–3× faster inference than A100 for transformer models
• Indicative cost in India:
• H100: ₹25–35 lakh per unit
• PCIe variant: ₹20–25 lakh
• SXM5 variant: ₹30–35 lakh
• Best suited for high-throughput deployments (e.g., 100,000+ queries/day) or multi-tenant internal platforms.

Quantization: Fitting Larger Models on Smaller GPUs

Quantization reduces numerical precision to lower memory usage and sometimes increase speed.

• FP16 (half precision)
• Standard for training and high-quality inference
• 7B model needs ~14GB VRAM
• INT8 (8-bit)
• ~50% memory reduction vs FP16
• Typically 1–2% quality loss on benchmarks
• 7B model needs ~7GB VRAM
• INT4 (4-bit)
• ~75% memory reduction vs FP16
• 3–5% quality loss on average
• 7B model needs ~3.5GB VRAM
• Often sufficient for many enterprise tasks: classification, summarization, RAG-based Q&A, internal copilots.
• GPTQ / AWQ
• Advanced 4-bit quantization schemes
• Better quality retention than naive INT4
• Recommended for production 4-bit deployments where you need to balance VRAM savings and answer quality.

GPU Procurement in India

• Direct from NVIDIA partners
• Distributors like Ingram Micro, Redington
• Typical 4–8 week lead times for A100/H100-class GPUs
• Cloud GPUs (for testing / POCs)
• AWS Mumbai, Azure Pune regions
• Good for benchmarking models and sizing before hardware purchase
• GPU-as-a-Service (Indian providers)
• Examples: Jarvislabs, E2E Networks
• Typical pricing: ₹80–200/hour per A100, depending on commitment and configuration
• Refurbished A100s
• Emerging secondary market in India
• 30–40% cost savings vs new