Run powerful LLMs locally on 4GB RAM using quantization

This tutorial details how to run capable small language models (SLMs) locally on systems with as little as 4GB of RAM. It addresses the common challenge of high memory requirements for LLMs by suggesting the use of optimized SLMs like Llama 3.2 3B or Microsoft Phi-3.5/Phi-4 Mini. The guide explains how quantization techniques, specifically using formats like GGUF to reduce model weights to 4-bit precision, drastically decrease memory footprint without significant performance loss. It provides step-by-step instructions using tools such as Ollama and llama.cpp to configure and run these models efficiently. AI

IMPACT Enables wider adoption of local AI models on resource-constrained devices, reducing cloud dependency and improving data privacy.

RANK_REASON The article provides a practical guide and tutorial for using existing tools and models to achieve a specific technical outcome (running LLMs on low RAM), rather than announcing a new model or research breakthrough.

Read on dev.to — LLM tag →

• GGUF
• Llama~3.2
• Llama 3.2:3b
• Llama-3.2-3B-Instruct
• llama.cpp
• Microsoft Phi-3.5
• Ollama
• Phi 4 Mini
• Q4_K_M
• small language model

• infra
• model release

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