What Q4 and Q5 mean
Quantization stores model weights with reduced precision to lower their memory and download requirements. In common GGUF filenames, Q4_K_M and Q5_K_M describe different quantization recipes used by llama.cpp.
Q4_K_M is usually the smaller choice for the same checkpoint and conversion family. Q5_K_M generally stores more precision and needs more space. The labels do not mean every value is stored in exactly four or five bits: scales, metadata and tensors treated at other precisions also contribute.
The llama.cpp quantization implementation defines these recipes. Different formats with a “4-bit” label, such as the MXFP4 expert weights in gpt-oss-20b, should not be treated as interchangeable.
A real download-size comparison
Qwen's official Qwen3 8B GGUF file list shows:
- Q4_K_M: about 5.03 GB.
- Q5_K_M: about 5.85 GB.
- Difference: about 0.82 GB, or 16% more weights than Q4_K_M.
These rounded decimal sizes are specific to that model and conversion. Q5 is not universally 16% or 25% larger. Compare the actual files for the checkpoint you want.
For the larger Qwen2.5-Coder 14B, the extra weight memory is more consequential on a tight GPU budget. The trade-off should be assessed alongside context, rather than choosing a quant by name alone.
File size is not total runtime memory
Loading a quantized model also needs working buffers and the context/KV cache. Some runtimes reserve capacity for the configured context when loading; others grow allocations as they process tokens.
Reducing the precision of the model weights does not automatically change the precision of the KV cache. These are separate settings in runtimes such as llama.cpp.
If Q5 fits at 4K but fails at 16K, the longer context may account for the difference. The download has not changed. See why context length affects memory.
When to start with Q4_K_M
Q4_K_M is a sensible initial test when memory is constrained, when you need to retain a useful context, or when Q5 would require additional CPU offload.
For example, an RTX 3060 12 GB running a larger model has less room for extra precision than the same card running Qwen3 8B. Check the runtime's actual allocation instead of assuming all installed VRAM is available.
A smaller quant that stays within the intended device budget may be more convenient than a larger one that changes the offload arrangement. This is a testable setup choice, not a universal speed ranking.
When Q5_K_M is worth testing
Try Q5_K_M when you have enough space for weights, context and buffers, and you want to compare whether it helps your workload. More stored precision can reduce quantization error, but it does not guarantee that every answer improves.
Use the same model version, chat template and generation settings. For coding, compare correctness with tests. For extraction, compare against known answers. For writing, judge the result against the constraints you actually care about.
Avoid comparing one Q4 model with a different Q5 model and attributing the whole difference to quantization.
A practical comparison
Begin with Q4_K_M and a representative context length. Record memory use, time to the first useful response and the outcome on several prompts. Repeat with Q5_K_M while keeping the other settings fixed.
If the Q5 setup changes CPU offload, record that too; it is part of the result. Keep the quant that delivers useful output within your memory and latency needs.
Compare memory estimates in the Finder. The final decision should use the exact artifact and runtime, rather than a generic claim that one quant is always best.