GGUF (GPT-Generated Unified Format) is a file format designed for storing and running large language models efficiently on consumer hardware. Created by the llama.cpp project, GGUF replaced the older GGML format and is now the standard for local AI inference.

What is Q4_K_M quantization?

Q4_K_M is a 4-bit quantization method that reduces model size by ~75% while maintaining excellent quality. The 'K' indicates k-quant (improved quantization), and 'M' means medium quality. It's the recommended quantization for most users, offering the best balance of size, speed, and quality.

How much RAM do GGUF models need?

RAM requirements depend on model size and quantization. With Q4_K_M: 7B models need ~6GB RAM, 13B models need ~10GB RAM, 1-3B models need 1-3GB RAM. Add 1-2GB for context and system overhead.

Is GGUF better than GGML?

Yes, GGUF replaced GGML and offers significant improvements: single-file format (no separate vocab files), extensible metadata, better compatibility, faster loading, and support for more model architectures. All modern tools use GGUF.

What is GGUF? Complete Guide to GGUF Format & Quantization (2025)

Q: What does GGUF stand for?

GGUF stands for GPT-Generated Unified Format. It's a binary file format optimized for fast loading and efficient inference of quantized language models.

What is GGUF?

GGUF (GPT-Generated Unified Format) is a file format designed for storing and running large language models (LLMs) efficiently on consumer hardware. Created by the llama.cpp project, GGUF is now the standard format for local AI inference.

Key Benefits of GGUF:

Run AI models on CPU without expensive GPUs
Quantization reduces model size by 50-75%
Single file contains everything needed
Works on Windows, Mac, and Linux
Supported by all major local AI tools

What Does GGUF Stand For?

GGUF stands for GPT-Generated Unified Format. The name reflects its purpose: a unified, standardized way to store AI models that were originally in various formats (PyTorch, SafeTensors, etc.).

GGUF Quantization Types Explained

Quantization reduces model precision to decrease file size and memory usage. Here are the common GGUF quantization types:

Quantization	Bits	Size Reduction	Quality	Best For
`Q4_K_M`	4-bit	~75%	Excellent	Recommended - Best balance
`Q4_K_S`	4-bit	~75%	Good	Low RAM systems
`Q5_K_M`	5-bit	~65%	Very Good	Quality-focused users
`Q5_K_S`	5-bit	~65%	Good	Balance with smaller size
`Q6_K`	6-bit	~55%	Excellent	Near-original quality
`Q8_0`	8-bit	~50%	Best	Maximum quality
`Q2_K`	2-bit	~85%	Lower	Extreme compression

💡 Recommendation: For most users, Q4_K_M offers the best balance of quality, speed, and memory usage. Use Q5_K_M or Q6_K if you have extra RAM and want better quality.

GGUF Memory Requirements

How much RAM do you need for different GGUF models? Here's a quick reference:

Model Size	Q4_K_M RAM	Q5_K_M RAM	Q8_0 RAM
1B parameters	~1 GB	~1.2 GB	~1.5 GB
3B parameters	~2.5 GB	~3 GB	~4 GB
7B parameters	~5-6 GB	~6-7 GB	~8-9 GB
13B parameters	~9-10 GB	~11-12 GB	~15 GB
70B parameters	~40 GB	~50 GB	~70 GB

⚠️ Important: Add 1-2 GB for context window and system overhead. For 16GB RAM systems, stick to models under 7B parameters with Q4_K_M quantization.

GGUF vs GGML: What's the Difference?

GGUF replaced the older GGML format in August 2023. Here's why GGUF is better:

Feature	GGML (Old)	GGUF (New)
File Structure	Multiple files needed	Single file
Metadata	Limited	Extensible key-value
Compatibility	Breaking changes	Forward compatible
Loading Speed	Slower	Faster
Tool Support	Deprecated	All modern tools

✅ Bottom Line: Always use GGUF format. GGML is deprecated and no longer supported by modern tools like llama.cpp, Ollama, and LM Studio.

Where to Download GGUF Models

GGUF models are available on HuggingFace. Popular sources include:

TheBloke - Thousands of quantized models
bartowski - High-quality quantizations
Qwen - Official Qwen GGUF models
Meta - Official Llama models

Look for files ending in .gguf with quantization suffix like Q4_K_M.gguf.

Tools That Support GGUF

GGUF Loader - Simple GUI for running GGUF models
llama.cpp - The original GGUF runtime
Ollama - Easy model management
LM Studio - Desktop app for local AI
GPT4All - Cross-platform local AI
KoboldCpp - For creative writing

What is GGUF? Complete Guide to GGUF Format