Guide: How to size local LLMs for GPU memory

A guide explains how to accurately estimate the memory requirements for running large language models locally, focusing on three key factors: model parameters, quantization, and context window size. The article details how parameter counts (e.g., '8b' for 8 billion) directly correlate to memory usage, with full precision models requiring more VRAM. It also covers quantization methods, like GGUF formats (Q4_K_M, Q8_0), which reduce model size by using fewer bits per weight, and emphasizes that lower quantization levels can be preferable to running a larger model with insufficient memory. Finally, the guide highlights the often-overlooked memory cost of the context window's KV cache, explaining how its size depends on model architecture and context length, and can be further managed through techniques like 8-bit caching and parallel request settings. AI

IMPACT Helps users optimize local LLM deployment by accurately estimating hardware requirements.

RANK_REASON The item provides practical guidance on optimizing the use of existing LLM technology on personal hardware.

Read on dev.to — LLM tag →

• gemma3:27b
• GGUF
• GPT OSS 20B
• graphics processing unit
• llama3.1:8b
• Llama-3.1:8b
• llama3.1:8b-instruct-fp16
• llama.cpp
• Ollama
• Qwen3 14B

• infra
• other

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