GGUF Quantization Explained: Q4_K_M vs Q8_0 and When Each Matters
Quantization shrinks LLM weights from float32 to int4 or int8 - here is exactly what each GGUF level means, how memory usage scales, and the quality tradeoffs.
Mahmudul Haque Qudrati
CEO & ML Engineer
April 22, 2026
7 min read
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A Llama 3.1 70B model in float32 takes 280 GB of RAM - impractical on any consumer hardware. Quantization reduces the precision of model weights from 32-bit or 16-bit floats to 8-bit or 4-bit integers. The result: dramatically lower memory requirements with a small, usually acceptable quality loss.
GGUF (GPT-Generated Unified Format) is the file format used by llama.cpp and tools like Ollama and LM Studio. It stores quantized weights with metadata about the quantization scheme.
Quantization Levels Explained
| Level | Bits | Method | Quality | Notes |
|---|---|---|---|---|
| Q4_0 | 4 | Simple block quant | Moderate | Fastest, lowest quality |
| Q4_K_M | 4 | K-quant, medium | Good | Best 4-bit for most use cases |
| Q4_K_S | 4 | K-quant, small | Moderate | Smaller than Q4_K_M |
| Q5_K_M | 5 | K-quant, medium | Very good | ~20% more RAM than Q4_K_M |
| Q6_K | 6 | K-quant | Near-lossless | Excellent for 13B and smaller |
| Q8_0 | 8 | 8-bit block quant | Near-perfect | 2x size of Q4, minimal quality loss |
| F16 | 16 | Half precision | Reference | ~2x Q8_0, baseline quality |
K-quant methods (K_M, K_S, K_L) use a smarter quantization scheme than naive Q4_0: they group weights into blocks and allocate higher precision to weights that matter more (typically attention layers). Q4_K_M is the community default for 4-bit inference.
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Memory Requirements by Model Size and Quant
| Model | Q4_K_M | Q5_K_M | Q8_0 | F16 |
|---|---|---|---|---|
| 7B | 4.1 GB | 5.0 GB | 7.7 GB | 14 GB |
| 13B | 7.4 GB | 9.0 GB | 14 GB | 26 GB |
| 34B | 19 GB | 23 GB | 36 GB | 68 GB |
| 70B | 40 GB | 48 GB | 75 GB | 140 GB |
Quality Impact on Benchmarks
On standard benchmarks (MMLU, HellaSwag, TruthfulQA), Q4_K_M loses 1 - 3% relative to F16 for 7B/13B models and 0.5 - 1.5% for 70B models. The larger the model, the more quantization-resistant it is - a Q4_K_M Llama 70B often outperforms an F16 Llama 13B despite using similar RAM.
Q8_0 is effectively lossless for most benchmarks - quality within 0.1 - 0.5% of F16 at half the memory.
GGUF vs GPTQ vs AWQ
| Format | Ecosystem | GPU support | CPU support |
|---|---|---|---|
| GGUF | llama.cpp, Ollama, LM Studio | Yes (partial offload) | Yes |
| GPTQ | AutoGPTQ, vLLM | GPU only | No |
| AWQ | vLLM, AutoAWQ | GPU only | No |
GGUF is unique in supporting CPU inference and partial GPU offloading - run a 70B model on a 16 GB GPU + CPU RAM combined.
How to Pick the Right Quantization
- Consumer GPU with 8 GB VRAM (RTX 4070, 3070): Q4_K_M for 7B or 8B models
- 16 GB VRAM (A4000, RTX 3090): Q4_K_M for 13B, or Q5_K_M for 7B if quality matters
- 24 GB VRAM (RTX 3090, A5000): Q4_K_M for 34B, or Q8_0 for 13B
- Apple M2/M3 36 GB unified: Q4_K_M for 70B or Q8_0 for 34B
- 2x A100 80 GB: F16 70B
Find GGUF models at HuggingFace - filter by model family and look for publishers like Bartowski or TheBloke.
#gguf#quantization#llama.cpp#memory#quality
Mahmudul Haque Qudrati
CEO & ML Engineer
Visionary leader with extensive experience in machine learning and software development. Drives strategic innovation and business growth.
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