Start with Q4_K_M when file size and memory headroom matter most, choose Q5_K_M when its extra size is manageable and you want a middle option, and choose Q8_0 when preserving fidelity is worth the larger footprint. None is a universal winner: model, quantization process, runtime memory, hardware, and workload all affect the choice.
How the three GGUF options compare
The llama.cpp project’s Llama 3 8B scoreboard provides a concrete comparison of model size and perplexity. These are measurements for that model and test setup, not guaranteed values for other GGUF files or a prediction of speed.
| Quantization | Model size | Perplexity | Scoreboard condition |
|---|---|---|---|
| Q4_K_M | 4.58 GiB | 6.382937 ± 0.039055 | Wikitext importance matrix (“WT 10m”) |
| Q4_K_M | 4.58 GiB | 6.407115 ± 0.039119 | No importance matrix |
| Q5_K_M | 5.33 GiB | 6.288607 ± 0.038338 | No importance matrix |
| Q8_0 | 7.96 GiB | 6.234284 ± 0.037878 | No importance matrix |
The scoreboard figures come from the llama.cpp Llama 3 8B table, revision f364eb6f. Its reported setup used CUDA, an AMD Epyc 7742 CPU, and one NVIDIA RTX 4090 GPU. The Q4_K_M result with an importance matrix and the Q5_K_M result without one were produced under different conditions, so that pair does not isolate the effect of quantization format alone. The table does not provide a controlled speed comparison for these formats. See the llama.cpp perplexity documentation and scoreboard.
What each quantization is best suited to
Q4_K_M: prioritize a smaller file
In the cited 8B example, Q4_K_M is the smallest of the three. It is a sensible starting point when storage or memory is tight, or when keeping more room for context and runtime overhead matters. That size advantage does not establish that Q4_K_M always runs faster; speed depends on the model, software, hardware, and backend.
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Q5_K_M: choose a middle ground
In the same example, Q5_K_M occupies more space than Q4_K_M and less than Q8_0. Consider it when the extra size is acceptable and your own task checks or relevant benchmarks indicate that the quality tradeoff is worthwhile. The scoreboard’s Q5_K_M perplexity is lower than its Q4_K_M result without an importance matrix, but the differing quantization conditions mean this is not proof of a fixed advantage for every model.
Q8_0: favor fidelity over footprint
Q8_0 is the largest option in the example. It has the lowest perplexity of these three in that scoreboard, making it a reasonable choice when preserving behavior matters more than file size and your deployment can accommodate it. It is still quantized, not lossless, and a lower perplexity score is not a universal guarantee of better answers.
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What perplexity tells you—and what it does not
The llama.cpp documentation explains: “The perplexity example can be used to calculate the so-called perplexity value of a language model over a given text corpus.” It also states: “Perplexity measures how well the model can predict the next token with lower values being better.” In a fixed-model, fixed-test comparison, it can help indicate the effect of quantization on next-token prediction.
Perplexity is not a direct measure of usefulness on your prompts. The documentation cautions that scores are not directly comparable between models, especially when tokenizers differ, and that results depend strongly on implementation details. It also notes that fine-tuned models can have higher perplexity even when people rate their outputs more highly. Judge the quantization on the tasks you care about—such as instruction following, reasoning, or factual responses—rather than treating the score as a final verdict.
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A 2026 preprint by Uygar Kurt evaluates a broader set of downstream reasoning, knowledge, instruction-following, and truthfulness benchmarks, as well as perplexity, CPU throughput, size, compression, and quantization time for Llama-3.1-8B-Instruct. That work illustrates why task and throughput measurements can matter alongside perplexity, but its conclusions apply to its single model and experimental setup, not to every GGUF model. Read the Llama-3.1-8B-Instruct preprint.
Check memory and workload before choosing
Model-file size is only one part of the deployment footprint. Runtime memory also has to accommodate context, the KV cache, and other overhead. Check the actual file you plan to use and leave room for the runtime; do not assume a model will fit just because its file is smaller than the device’s advertised memory.
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- File size: Compare the exact GGUF files available for your model, not just the quantization labels.
- Runtime memory: Account for context length, KV cache, and software overhead in addition to model weights.
- Quality: Compare outputs using representative prompts and tasks from your own workload.
- Speed: Measure on your own hardware and backend if throughput matters; the cited scoreboard does not establish a speed ranking.
When comparing downloads, verify the model name, quant type, source, and any conversion or importance-matrix notes. A scoreboard entry is most useful when its model and quantization conditions match the file you are considering.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Quantization provenance can change the result
The llama.cpp quantization guide describes converting a source model into GGUF and then applying llama-quantize; its example uses Q4_K_M. It warns that requantizing already-quantized tensors can severely reduce quality compared with quantizing from 16-bit or 32-bit input. A suitable importance matrix can reduce some quantization loss, so the conversion history and matrix conditions matter when interpreting a file or benchmark. See the llama.cpp quantization guide.
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