How to Get Started with a Private LLM

Introduction

Large Language Models (LLMs) like ChatGPT have transformed how businesses use artificial intelligence, offering powerful capabilities in content generation, customer support, and data analysis. However, reliance on public APIs comes with limitations—data privacy risks, lack of customization, and unpredictable costs.

Private LLMs offer an alternative: language models hosted and managed within your own infrastructure. These models give organizations full control over data, performance, and security. Whether for regulatory compliance, competitive differentiation, or specialized workflows, companies are increasingly adopting private deployments.

This guide walks through the key considerations—from choosing the right model to deployment options—so you can make informed decisions about bringing AI in-house.

What Is a Private LLM?

A Private LLM is a large language model that runs on your organization’s internal infrastructure or in a private cloud environment.

Data remains under your control – Critical for sectors such as healthcare, finance, and legal services.
Customization – You can fine-tune models using internal documents or industry-specific terminology.
Predictable costs – Avoid variable per-query fees common with public APIs.

Private LLMs range from open-source models like Llama 3 and Mistral to enterprise-adapted versions tailored for specific business needs.

When Should You Consider Using a Private LLM?

Strict Compliance Requirements: If your operations are governed by regulations such as GDPR, HIPAA, or SOC 2, keeping data within your network becomes essential.
Sensitive Data Handling: Applications involving confidential contracts, internal research, or customer interactions benefit from the added layer of data isolation.
High-Volume Usage: For companies making millions of API calls monthly, hosting your own model may be more cost-effective than relying on external providers.
Custom Workflows: Need a model adapted to your domain? Private LLMs allow deep customization and integration into existing systems.

If your use case involves general-purpose tasks without sensitive data, public APIs may still be sufficient.

What Models Are Available?

Meta Llama 3 (8B / 70B) – A high-performance family of models available for both research and commercial use.
Mistral 7B – Lightweight and efficient, ideal for production environments.
Falcon 180B – Designed for complex, resource-intensive tasks.
Phi-3 (Microsoft) – Compact and optimized for edge computing and low-resource environments.
Gemma (Google) – Lightweight open-weight models suited for development and experimentation.

Most businesses start with mid-sized models (7B–13B parameters), which balance capability with hardware demands.

Hardware and Infrastructure Requirements

Running a large language model locally requires significant computational power. Exact requirements vary by model size:

Model Size	Minimum GPU	RAM	Storage
1.5B	NVIDIA GEFORCE RTX 3050	4–6GB	25 GB+
Up to 7B	RTX 3080/3090	12GB	50 GB+
13–20B	NVIDIA A100 (40GB)	64GB	100 GB+
70B+	Multi-GPU	128GB+	250 GB+

Cloud-based GPU instances (e.g., AWS EC2, Azure NDv5) are useful for testing and scaling.
Quantized models reduce hardware requirements but may impact accuracy.
Smaller models can run on CPUs, though performance will be limited.

Deployment Options

On-Premise
Pros: Maximum data control, ideal for air-gapped networks.
Cons: Higher upfront investment; ongoing maintenance required.
Private Cloud
Pros: Easier scalability and management; no physical hardware needed.
Cons: Recurring costs; dependency on cloud provider.

Hybrid setups are also common—development and testing in the cloud, production on-premise.

Security and Privacy Advantages

Inputs and outputs remain within your network.
Full logging and access controls support compliance and governance.
Can be deployed behind firewalls or in fully air-gapped environments.

For regulated industries, these features are often mandatory rather than optional.

Cost Considerations

Hardware: High-end GPUs can cost $5,000–$20,000 each.
Cloud Costs: GPU instances typically range from $1–$5/hour.
Operational Overhead: Requires DevOps or MLOps expertise for deployment and maintenance.

Rule of thumb: Private LLMs become cost-effective at scale or when data privacy is non-negotiable.

First Steps: A Practical Checklist

Define Your Use Case: Identify a specific application—internal knowledge base, customer chatbot, document processing.
Assess Data Sensitivity: Determine whether data must stay internal and any compliance implications.
Choose a Model: Begin with smaller models like Mistral 7B or Llama 3 8B to test feasibility.
Evaluate Infrastructure Options: Decide between cloud-based GPU instances or on-premise deployment based on budget and technical capacity.
Select Deployment Tools: Try Ollama for local prototyping or vLLM for scalable inference.
Run a Pilot: Deploy in a limited environment to gather feedback and measure performance.
Plan for Scaling: Establish monitoring, logging, and access controls early in the process.

Conclusion

Private LLMs offer clear benefits for organizations prioritizing data control, customization, and long-term cost efficiency. While not every company needs one, those with regulatory obligations or specialized AI workflows should consider self-hosted solutions.

Start small: experiment with open-source models using cloud GPUs, then scale as you validate results. The goal is to align AI deployment with your infrastructure, security policies, and business goals.

Next step: Identify one high-impact use case and run a pilot. This will clarify whether a private LLM is a strategic fit for your team.