Self-Hosting Small Language Models: A Complete Guide

One of the key benefits of Small Language Models is the ability to run them on your own infrastructure. Here's how to do it effectively.

Hardware Requirements

SLMs are designed to run on modest hardware:

For CPU-only deployment, expect 2-5x slower inference.

Serving Frameworks

Several frameworks are available for serving LLMs:

vLLM

Best for high-throughput serving with continuous batching.

Text Generation Inference (TGI)

Production-ready with built-in optimizations.

Ollama

Simple local deployment, great for development.

Optimization Techniques

Quantization

Reduce model precision from FP16 to INT8 or INT4 for 2-4x speedup.

KV Cache Optimization

Reuse computed key-value pairs for faster generation.

Speculative Decoding

Use a smaller draft model to speed up generation.

OpenTracy Integration

OpenTracy exports models in formats compatible with all major serving frameworks. One-click export to GGUF, ONNX, or TensorRT.