The Complete Technical Guide to Running LLMs Locally in 2026

2026-07-22 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

This technical guide offers a comprehensive approach to running Large Language Models (LLMs) locally, moving beyond basic setup instructions. It delves into critical aspects such as hardware math, the practical tradeoffs of quantization, and a detailed benchmark comparison of five different inference engines. The guide also includes two real-world case studies, conducted on a 16GB Apple Silicon Mac using Ollama, to validate theoretical claims against actual performance. While the hardware math and case studies are based on the author's direct testing, broader engine comparisons, including vLLM, text-generation-webui, and SGLang, are researched and cited, providing a clear distinction between tested and evidence-based information.

Key takeaway

For AI Engineers evaluating local LLM deployment, this guide provides essential technical depth beyond basic setup. You should scrutinize hardware math, quantization impacts, and benchmark diverse inference engines like Ollama, vLLM, and SGLang to ensure practical performance aligns with theoretical claims. This approach helps you avoid the pitfalls of incomplete benchmarks and make informed decisions for robust local LLM integration.

Key insights

A comprehensive guide to local LLM deployment, balancing hardware math, quantization, and inference engine benchmarks.

Principles

• Benchmarks often omit crucial details.
• Distinguish personal tests from cited evidence.

Method

Analyzes hardware math, quantization tradeoffs, and benchmarks five inference engines, validated by two real-world case studies on a 16GB Apple Silicon Mac.

In practice

• Evaluate quantization costs against performance.
• Benchmark multiple inference engines like vLLM and Ollama.
• Validate theoretical benchmarks with real-world tests.

Topics

• Local LLM Deployment
• Quantization
• Inference Engines
• Hardware Benchmarking
• Ollama
• vLLM
• SGLang

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.