llama3.1:8b-instruct-q4_K_M and llama3.1:8b-instruct-q4_1 are quantizations of Meta's LLaMA 3 (8B) model, but the main difference is that they are compressed using different quantization methods. Below is a clear summary of the differences.
✅ Common points
- Base model: LLaMA 3 8B Instruct
- Quantized (weighted) model for inference
- Usually distributed in GGUF format and used in llama.cpp or Ollama
🔍 Details of the difference
| Item | q4_K_M | q4_1 |
|---|---|---|
| Quantization Method | Q4_K_M (K-block quantization + M = mix accuracy) | Q4_1 (4-bit quantization) |
| Accuracy | Slightly high (mixed float accuracy) | Basic accuracy (simple 4bit) |
| Memory usage | Large (to maintain accuracy) | Low (simple quantization) |
| Inference speed | Slightly slow (complex calculations) | Fast |
| Aptitude for applications | For QA and inference that emphasizes accuracy | For applications that emphasize light and response speed |
| Model size | Large (approx. 4.6GB) | Small (approx. 4.2GB) |
🧠 Commentary (roughly)
- q4_1: Classic 4-bit quantization. It's light and fast, but the accuracy of inference is slightly reduced.
- q4_K_M: An improved version that incorporates optimization for each K block + Mixed Precision (such as float16). It's highly accurate, but it costs memory and speed.
🏁 Which should I choose?
- If memory or speed is constrained in the local environment → q4_1
- Preferring accuracy, QA and RAG applications → q4_K_M