Ollama VPS Hosting India — Run Private LLMs from ₹3,600/month

Self-host Llama, Qwen, Gemma, Mistral, DeepSeek and gpt-oss on your own server in India and stop paying per token. LLM inference holds every core at 100% for as long as the model is talking, which is exactly the workload an oversold VPS host throttles. We do not overallocate CPU: the AMD EPYC 7C13 vCores on your plan are there when the model needs them, so tokens per second are the same at 3pm as at 3am. Plans from 16GB to 512GB RAM, NVMe SSD for fast model loads, 1Gbps unmetered for pulling weights, and full root SSH for Ollama, Open WebUI and your own RAG stack. Prompts and documents never leave our ISO 27001 certified New Delhi datacenter. Provisioned in 15 minutes.

Plans Features Specs Setup FAQs

Choose Your Ollama VPS Plan

amd
IN-LITE
Starting at:
₹3,600/month
Order Now
  • CPU
    AMD EPYC 7C13
  • vCores
    6 vCore @ Upto 3.7GHz
  • RAM
    16GB DDR4
  • DRIVES (NVMe SSD)
    160GB NVMe SSD
  • UPLINK
    1Gbps Unmetered
    ● 534 Tbps Magic Transit
amd
IN-PLUS
Starting at:
₹7,040/month
Order Now
  • CPU
    AMD EPYC 7C13
  • vCores
    12 vCore @ Upto 3.7GHz
  • RAM
    32GB DDR4
  • DRIVES (NVMe SSD)
    320GB NVMe SSD
  • UPLINK
    1Gbps Unmetered
    ● 534 Tbps Magic Transit
amd
IN-PREMIUM
Starting at:
₹10,560/month
Order Now
  • CPU
    AMD EPYC 7C13
  • vCores
    16 vCore @ Upto 3.7GHz
  • RAM
    48GB DDR4
  • DRIVES (NVMe SSD)
    480GB NVMe SSD
  • UPLINK
    1Gbps Unmetered
    ● 534 Tbps Magic Transit
amd
IN-ELITE
Starting at:
₹14,080/month
Order Now
  • CPU
    AMD EPYC 7C13
  • vCores
    24 vCore @ Upto 3.7GHz
  • RAM
    64GB DDR4
  • DRIVES (NVMe SSD)
    640GB NVMe SSD
  • UPLINK
    1Gbps Unmetered
    ● 534 Tbps Magic Transit
amd
IN-TURBO
Starting at:
₹22,160/month
Order Now
  • CPU
    AMD EPYC 7C13
  • vCores
    48 vCore @ Upto 3.7GHz
  • RAM
    128GB DDR4
  • DRIVES (NVMe SSD)
    1.28TB NVMe SSD
  • UPLINK
    1Gbps Unmetered
    ● 534 Tbps Magic Transit
amd
IN-CLASSIC
Starting at:
₹50,720/month
Order Now
  • CPU
    AMD EPYC 7C13
  • vCores
    64 vCore @ Upto 3.7GHz
  • RAM
    256GB DDR4
  • DRIVES (NVMe SSD)
    2.56TB NVMe SSD
  • UPLINK
    1Gbps Unmetered
    ● 534 Tbps Magic Transit
amd
IN-ULTRA
Starting at:
₹98,440/month
Order Now
  • CPU
    AMD EPYC 7C13
  • vCores
    128 vCore @ Upto 3.7GHz
  • RAM
    512GB DDR4
  • DRIVES (NVMe SSD)
    5.12TB NVMe SSD
  • UPLINK
    1Gbps Unmetered
    ● 534 Tbps Magic Transit

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Ollama LLM API running on a VPS in India

One Command to a Working LLM API

Ollama installs with a single script, registers itself as a systemd service and starts serving an HTTP API on port 11434. Pull a model with ollama pull llama3.1:8b and you have an endpoint your code can call. It also exposes an OpenAI-compatible route at /v1, so an application already written against the OpenAI SDK usually needs nothing more than a new base URL. Add Open WebUI in Docker and your whole team gets a ChatGPT-style interface with accounts, chat history and document upload, all running on a server you control. Full root SSH means none of this fights a control panel.

CPU Inference Only Works on a Host That Does Not Oversell

A web server touches the CPU in short bursts. An LLM is the opposite: generating a reply keeps every core you have at 100% until the last token, and speed is set by how fast the CPU can stream the model's weights out of memory. On an oversold host that sustained load is the first thing to be throttled, and you see it as replies that crawl in the evening and fly at night. You can test any provider for this yourself: run top while a model is generating and read the st value, which is CPU time stolen by other tenants. On our nodes it should sit at or near zero, because we do not overallocate CPU or RAM. That is the single most important spec for LLM hosting, and it is the one that never appears on a pricing table.

AMD EPYC vCores that are not oversold for consistent LLM tokens per second
Private LLM prompts and documents hosted in India for DPDP compliance

Prompts, Documents and Embeddings Stay in India

Whatever you paste into a hosted AI API leaves your control: contracts, patient notes, source code, customer conversations, financial records. With Ollama on your own VPS the model runs next to the data and nothing is sent to a third party, which is the simplest possible answer to a client's security questionnaire and to your obligations under the Digital Personal Data Protection Act. The cost model changes too. API pricing is per token in US dollars and grows with every user you add. A VPS is one fixed rupee amount, paid by UPI, card or net banking, whether the model answers a hundred prompts a day or runs a batch job flat out all month.

Why AI Teams Run Ollama Here

LLM hosting is decided by three things: how much RAM you have, whether the CPU is really yours, and where the data sits.

16GB to 512GB RAM

A model has to fit in memory with room for context. 16GB runs 7B to 8B models, 32GB runs 14B and the efficient mixture-of-experts models, 64GB and up runs 32B to 70B. Upgrade in place as your models grow.

EPYC Cores, Not Oversold

AMD EPYC 7C13 at up to 3.7GHz with no CPU overallocation. Inference is a sustained 100% load, so consistent cores are what turn into consistent tokens per second.

NVMe Model Loads

Model files run from 2GB to over 60GB. NVMe SSD loads an 8B model into RAM in seconds, so switching between models or restarting the service does not mean a coffee break.

Private by Design

Ollama listens on localhost only by default and has no login of its own. Keep port 11434 closed and publish it through an authenticated HTTPS reverse proxy. Your prompts never touch a third-party API.

No Per-Token Billing

One fixed monthly price in rupees. Run embeddings over a million documents or leave a summarisation job going all weekend: the bill does not move.

534 Tbps Magic Transit

If you publish a chatbot or an API on top of your model, it is a target. Cloudflare Magic Transit sits upstream of every server at no extra cost, so an attack does not take your AI product offline.

How to Install Ollama on a VPS in India

From order to a working private LLM in about twenty minutes.

1. Size the RAM to the model

At 4-bit quantisation budget about 6GB for an 8B model, 11GB for 14B, 20GB for 30B to 32B and 45GB for 70B, plus room for context. IN-LITE (16GB, ₹3,600/month) suits 7B to 8B models. IN-PLUS (12 vCores, 32GB, ₹7,040/month) is where most teams should start. IN-ELITE (64GB) and IN-TURBO (128GB) carry the large models.

2. Deploy Ubuntu 24.04 LTS

Order the plan, choose Ubuntu 24.04 LTS or 22.04 LTS and pay by UPI, card, net banking or crypto. The server is provisioned within 15 minutes. SSH in as root and run apt update && apt upgrade -y.

3. Install Ollama

Run curl -fsSL https://ollama.com/install.sh | sh. The script installs the binary, creates an ollama systemd service and starts the API on 127.0.0.1:11434. It detects that there is no GPU and uses the CPU automatically.

4. Pull and run a model

Start with ollama run llama3.1:8b or ollama run gemma3:12b for a chat prompt in the terminal. On 32GB try ollama run gpt-oss:20b. For RAG, pull an embedding model with ollama pull nomic-embed-text.

5. Add Open WebUI for your team

Install Docker, then run the Open WebUI container and point it at the Ollama API on the host. Your team gets a browser chat interface with user accounts, history and document upload, backed entirely by your own server.

6. Secure and tune it

Leave port 11434 closed in the firewall. Put Caddy or Nginx in front with HTTPS and authentication before exposing anything. Set OLLAMA_KEEP_ALIVE so the model stays loaded between requests and OLLAMA_NUM_PARALLEL to match how many users you serve at once.

Ollama VPS Hosting India — Frequently Asked Questions

Yes. Ollama falls back to CPU inference automatically when no GPU is present, and quantised models up to around 14B parameters are genuinely usable that way. What matters is enough RAM to hold the model and CPU cores that are not being shared out from under you. A GPU becomes necessary when you need fast interactive replies from 70B-class models or you are serving many simultaneous chat users.

The model has to fit in memory with headroom for the context window and the operating system. At the default 4-bit quantisation, budget roughly: 3B to 4B models, 4GB. 7B to 8B models, 6GB. 12B to 14B models, 11GB. 30B to 32B models, 20GB. 70B models, 45GB. gpt-oss 120B, about 65GB. In plan terms: IN-LITE (16GB) for 7B to 8B, IN-PLUS (32GB, ₹7,040/month) for 14B models and gpt-oss 20B with long context, IN-PREMIUM (48GB) for 32B, IN-ELITE (64GB) for 70B and IN-TURBO (128GB) for gpt-oss 120B or several models loaded at once.

Typical CPU-only figures for a 4-bit 7B to 8B model are in the range of 8 to 15 tokens per second, which is about reading speed and comfortable for chat, summarisation and background automation. A dense 14B model lands in the mid single digits. Speed is limited by memory bandwidth more than by core count, so it depends on the model, the quantisation and the context length. We would rather you measure it than take a number on trust: run ollama run llama3.1:8b --verbose and Ollama prints the exact eval rate.

Mixture-of-experts models are the sweet spot for CPU inference. gpt-oss 20B activates only about 3.6B parameters per token and Qwen3 30B-A3B about 3B, so they need the RAM of a large model but run at close to the speed of a small one. That is why a 32GB plan is such good value: it fits models that answer like a 20B to 30B model and generate like a 4B. For dense models, Llama 3.1 8B, Qwen3 8B, Gemma 3 12B and Phi-4 14B are all solid choices, and small models such as Llama 3.2 3B or Gemma 3 4B are ideal for classification and extraction at high throughput.

Because inference is a sustained 100% load on every core, not a burst. Hosts that sell the same physical cores to many customers rely on nobody using them fully, and they throttle the tenants who do. The symptom is tokens per second that swing by the hour. We do not overallocate CPU or RAM. Verify any host, including us, by running top during generation and watching the st (steal) column: anything consistently above a few percent means you are paying for cores you are not getting.

It depends on volume, and we would rather be straight about it. For a few hundred requests a day, a hosted API is usually cheaper and the frontier models are stronger. Self-hosting wins when the workload is steady or heavy (embeddings over large document sets, classification, summarisation pipelines, an internal assistant used all day), when the data cannot leave your control, or when you need a fixed rupee cost you can quote to a client instead of a dollar bill that scales with usage. Many teams run both: a local model for private and bulk work, an API for the hardest questions.

Yes. The model weights, your prompts, uploaded documents, embeddings and chat history all live on your VPS in the ISO 27001 certified Advika Datacenter in New Delhi. Nothing is sent to a model vendor. For legal, healthcare, finance and government-adjacent work that is often the deciding factor, and it keeps personal data processing inside India for the Digital Personal Data Protection Act.

Ollama has no authentication of its own, so never open port 11434 to the internet. By default it binds to 127.0.0.1, which is safe. If other machines need it, either reach it over an SSH tunnel or a private VPN such as WireGuard or Tailscale, or put Caddy or Nginx in front with HTTPS and an API key or basic auth. Open WebUI has its own user accounts and should be the only thing you publish.

Yes. Anything that can call an HTTP API can use it. n8n has native Ollama nodes for AI workflows, OpenClaw can use a local Ollama model as its brain, and LangChain, LlamaIndex and the OpenAI SDKs all connect to the /v1 endpoint. Running the model and the application on the same VPS keeps every call on localhost, with no network latency and no data leaving the machine. Size for both: IN-PLUS (32GB) is a sensible floor for a model plus an application stack.

The instant-deploy plans on this page are CPU-only. If you need 70B-class models at interactive speed, high concurrency, or a whole EPYC machine to yourself, look at our AMD EPYC dedicated servers in New Delhi or write to business@inservers.com with the model and the number of users you need to serve, and we will size it with you.

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