How to Host Your Own LLM: Self-Hosted AI Infrastructure Guide

Running your own large language model gives you complete control over your AI infrastructure: no API costs, no rate limits, full data privacy, and the ability to customize models for your specific needs. This guide covers everything you need to deploy self-hosted LLMs effectively.

Why Self-Host an LLM?

Data Privacy and Compliance

When you use API-based AI services, your prompts and data pass through third-party servers. Self-hosting ensures:

• Sensitive data never leaves your infrastructure
• Compliance with regulations requiring data residency
• No risk of training data being used by providers
• Full audit trail of all AI interactions

Cost Control

API costs scale linearly with usage. Self-hosting provides predictable costs:

• High-volume usage becomes dramatically cheaper
• No per-token surprises
• Fixed infrastructure cost regardless of usage
• Break-even depends on your usage volume

Customization and Control

• Fine-tune models on your specific data
• No content filtering or usage restrictions
• Choose exactly which model versions to run
• Optimize for your specific latency/throughput needs

Hardware Requirements by Model Size

7B Parameter Models (Mistral 7B, Llama 2 7B)

• Minimum: 16GB VRAM, 32GB RAM
• Recommended: 24GB VRAM, 64GB RAM
• Throughput: 20-50 tokens/second on RTX 4090
• Best for: Chat applications, code assistance, summarization

13B-30B Parameter Models

• Minimum: 24-48GB VRAM
• Recommended: 48GB+ VRAM or quantized versions
• Better reasoning and instruction following than 7B
• Good balance of capability and resource requirements

70B+ Parameter Models (Llama 2 70B, Mixtral 8x7B)

• Minimum: 80GB+ VRAM (A100 80GB or 2x 48GB GPUs)
• Quantized: Can run on 48GB with 4-bit quantization
• Near-GPT-4 quality for many tasks
• Requires significant investment but offers best open-source performance

Popular Open-Source Models

General Purpose

• Llama 3: Meta's latest, excellent all-around performance
• Mistral/Mixtral: Efficient architecture, great for its size
• Qwen: Strong multilingual capabilities

Code-Specialized

• CodeLlama: Optimized for code generation and understanding
• DeepSeek Coder: Competitive coding performance
• StarCoder: Open-source code model with permissive license

Instruction-Tuned Variants

• Look for "instruct" or "chat" versions of base models
• Better at following instructions and conversation
• Community fine-tunes often available (check Hugging Face)

Deployment Software Options

vLLM

High-performance inference engine optimized for throughput:

• PagedAttention for efficient memory use
• Continuous batching for high throughput
• OpenAI-compatible API
• Best for: Production deployments needing maximum throughput

llama.cpp

CPU and GPU inference with excellent quantization support:

• Runs on CPU (slower but no GPU required)
• Excellent 4-bit and 8-bit quantization
• Low memory footprint
• Best for: Resource-constrained environments, CPU-only servers

Text Generation Inference (TGI)

Hugging Face's production inference server:

• Optimized for Hugging Face models
• Built-in quantization and batching
• Docker-ready deployment
• Best for: Teams already using Hugging Face ecosystem

Ollama

Simple local deployment tool:

• One-command model downloads and serving
• Easy model management
• Good for development and testing
• Best for: Getting started quickly, local development

Deployment Architecture

Single Server Setup

Simplest approach for most use cases:

• One GPU server running inference
• Direct API access or through reverse proxy
• Suitable for internal tools, moderate traffic

Load-Balanced Setup

For higher availability and throughput:

• Multiple GPU servers behind load balancer
• Health checks and automatic failover
• Horizontal scaling by adding servers

Hybrid Approach

Combine self-hosted and API for flexibility:

• Self-hosted for baseline/sensitive workloads
• API fallback for overflow or specific capabilities
• Route based on request type or load

Quantization: Running Larger Models on Less Hardware

Quantization reduces model precision to save memory:

• FP16: Half precision, minor quality loss, 2x memory savings
• INT8: 8-bit integers, ~4x memory savings, small quality impact
• INT4/GPTQ/AWQ: 4-bit, ~8x savings, noticeable but often acceptable quality loss

A 70B model that normally needs 140GB VRAM can run in ~35GB with 4-bit quantization, making it accessible on consumer hardware.

Self-Hosted vs API: When to Choose Each

API-Based Services

Cloud APIs charge per token, which scales linearly with usage. This works well for low-volume or variable workloads but can become expensive at scale.

Self-Hosted Infrastructure

Self-hosting provides fixed infrastructure costs regardless of token volume. The break-even point depends on your usage patterns, but high-volume users often see significant savings.

When Self-Hosting Wins

• High volume (>500K tokens/day)
• Predictable, steady usage
• Data privacy requirements
• Need for customization/fine-tuning

Security Considerations

• Network isolation: Keep LLM servers on private network
• Authentication: Require API keys for all requests
• Input validation: Sanitize prompts to prevent injection
• Output filtering: Implement safety checks as needed
• Logging: Audit all requests for compliance

Getting Started Checklist

1. Define your use case and performance requirements
2. Select appropriate model size based on quality needs
3. Choose hardware that fits model requirements
4. Set up inference software (vLLM recommended for production)
5. Implement monitoring and logging
6. Test thoroughly before production deployment
7. Plan for updates and model upgrades

Conclusion

Self-hosting LLMs offers compelling advantages for organizations with significant AI usage, data privacy requirements, or need for customization. While it requires more initial setup than API services, the long-term benefits of cost control, privacy, and flexibility often outweigh the complexity.

At Packet25, we offer GPU-equipped servers ideal for LLM hosting, with Swiss data protection ensuring your AI infrastructure meets the highest privacy standards. Contact us to discuss your self-hosted AI requirements.