How to Host Your Own LLM on AWS or Azure: Self-Hosted LLM Guide (2026)

Auther : Infranexa team

Date: October 22, 2026

To host your own LLM, choose an open-weight model (such as Llama, Mistral, or Qwen), launch a GPU instance on AWS or Azure with enough VRAM, install an inference engine like vLLM or Ollama, and expose it through a private, secured API endpoint. A self-hosted LLM gives you full data control and predictable costs at scale.

What Is a Self-Hosted LLM?

A self-hosted LLM is a large language model that runs on infrastructure you control, instead of being accessed through a third-party API like OpenAI. You download the model weights, run them on your own cloud servers or on-premise hardware, and serve responses through your own endpoint.

There are three common ways to host one:

Option	Where it runs	Best for
Cloud self-hosted LLM	GPU instances on AWS or Azure	Most businesses: scalable, no hardware to buy
On-premise LLM	Your own data center servers	Strict compliance or air-gapped environments
Local LLM	A personal computer or workstation	Testing, prototyping, individual developers

This guide focuses on the cloud option, since it offers the best balance of control, speed, and cost for growing teams.

Why Host Your Own LLM?

Companies are moving toward private LLM setups for four main reasons:

Data privacy: Prompts and outputs never leave your cloud account. This matters for healthcare, finance, legal, and any team handling customer data under SOC 2 or HIPAA requirements.
Cost control at scale: API pricing charges per token. Once your usage is high and steady, a dedicated GPU server often costs less than paying per request.
Customization: You can fine-tune the model on your own data, using techniques like LoRA, and connect it to internal documents through RAG (retrieval-augmented generation).
No vendor lock-in: You choose the model, version, and update schedule. Price changes or model deprecations from an API provider no longer affect you.

What You Need to Host Your Own LLM

Every self-hosted LLM setup has three core parts.

1. An Open-Weight Model

Popular choices include Llama, Mistral, Qwen, DeepSeek, and Gemma, most of which are available on Hugging Face. Always check the license before commercial use, since terms vary between model families.

2. A GPU with Enough VRAM

VRAM (GPU memory) is the main limit on which models you can run. A simple rule of thumb:

16-bit precision: about 2 GB of VRAM per billion parameters. A 7B model needs roughly 14 to 16 GB.
4-bit quantization: about 0.5 to 0.7 GB per billion parameters. A 7B model fits in about 5 GB, and a 70B model in about 40 GB.

Quantization formats like GGUF, AWQ, and GPTQ shrink a model’s memory footprint with only a small quality trade-off.

3. An LLM Inference Engine

LLM inference is the process of running a trained model to generate responses. An inference engine turns your model into a working LLM server.

Inference engine	Strength	Best for
vLLM	High throughput, OpenAI-compatible API	Production workloads
Ollama	Very simple setup	Development and small teams
Hugging Face TGI	Tight Hugging Face integration	Teams already using Hugging Face
NVIDIA Triton / TensorRT-LLM	Maximum GPU performance	High-scale, latency-sensitive apps

Which Is the Best Self-Hosted LLM?

The best self-hosted LLM depends on your use case and budget:

General chat and support bots: 7B to 14B instruction-tuned models run fast on a single 24 GB GPU.
Coding assistants: Code-specialized models from the Qwen and DeepSeek families perform well.
Complex reasoning: 70B-class models deliver stronger results but need multiple GPUs or heavy quantization.

Start with the smallest model that meets your quality bar. Then move up only if testing shows you need to.

How to Host Your Own LLM on AWS

AWS gives you three paths, from fully managed to full control.

Option 1: Amazon EC2 (full control).

This is the most flexible way to self-host.

Launch a GPU instance. G5 (NVIDIA A10G) and G6 (NVIDIA L4) instances suit 7B to 14B models, while P4d (A100) and P5 (H100) handle large models.
Use an AWS Deep Learning AMI, which comes with NVIDIA drivers preinstalled.
Install vLLM and start the server.
Place the instance in a private VPC, put an API gateway or load balancer in front of it, and add authentication.
Set up autoscaling and monitoring with Amazon CloudWatch.

pip install vllm

vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000

Option 2: Amazon SageMaker (managed endpoints).

SageMaker deploys open-source models to managed inference endpoints with built-in scaling. You give up some flexibility, but the operations work is much lighter.

Option 3: Amazon Bedrock (fully managed).

Bedrock offers hosted foundation models, and its Custom Model Import feature supports some open-weight models. It’s the easiest option, but it’s not truly self-hosted, so you have less control over infrastructure and costs.

For containerized, multi-model setups, many teams run vLLM on Amazon EKS (Kubernetes) with GPU node pools.

How to Deploy an LLM on Azure

Azure follows a similar pattern:

Azure GPU VMs: NC-series and ND-series virtual machines (including A100 and H100 options) give you full control. The setup steps match the EC2 process above.
Azure Machine Learning managed online endpoints: Deploy models with managed scaling and monitoring.
Azure AI Foundry: Browse a model catalog and deploy open models to managed compute with minimal setup.
Azure Kubernetes Service (AKS): Run inference engines in containers with GPU node pools for production scale.

How Much Does It Cost to Host Your Own LLM?

LLM costs depend on four factors: GPU instance type, hours of uptime, traffic volume, and engineering time.

Approach	Pricing model	Cost-effective when
LLM API (OpenAI, etc.)	Pay per token	Usage is low or unpredictable
Managed (Bedrock, SageMaker)	Per token or per instance hour	You want less operational work
Self-hosted on EC2 or Azure VMs	Per GPU hour	Usage is high and steady

To estimate your break-even point, compare your monthly LLM token cost on an API with the monthly price of a GPU instance running 24/7. Spot instances and reserved capacity can cut GPU costs significantly for suitable workloads.

LLM Inference Optimization Tips

Quantize your model to fit smaller, cheaper GPUs.
Use continuous batching (built into vLLM) to serve many users at once.
Track tokens per second and latency to right-size your instances.
Scale to zero in off-hours for internal tools.
Cache frequent responses to reduce repeated inference.

Frequently Asked Questions

How do I host my own LLM on AWS?

Launch an EC2 GPU instance, such as G5 or G6, using a Deep Learning AMI. Then install vLLM, load an open-weight model from Hugging Face, and expose it through a secured API inside a private VPC.

Is self-hosting an LLM cheaper than using the OpenAI API?

It can be, at high and steady usage. For low or irregular traffic, pay-per-token APIs are usually cheaper because you don’t pay for idle GPU time.

What GPU do I need to run an LLM in the cloud?

A 24 GB GPU (A10G or L4) handles most 7B to 14B models. Models with 70B parameters need about 40 GB at 4-bit quantization, or multiple GPUs at full precision.

Amazon Bedrock vs a self-hosted LLM: which should I choose?

Choose Bedrock for speed and minimal operations. Choose self-hosting for full data control, custom fine-tuning, and lower costs at scale.

Can I run a private LLM without sending data to OpenAI?

Yes. A self-hosted LLM keeps all prompts and outputs inside your own cloud account or on-premise servers.

What is LLM inference?