Great question! Both are quantization formats for GGUF models, but they make different tradeoffs between size, speed, and quality. Q4_K_M (4-bit K-quant, medium): - Stores weights at ~4.5 bits per parameter on average - Uses a mixed-precision scheme where attention layers get slightly higher precision - Roughly 40-50% the size of the original F16 model - Quality loss is minimal for most tasks — typically <1% perplexity increase - Best for: running 7B-13B models on consumer GPUs with 6-8GB VRAM Q8_0 (8-bit linear): - Stores weights at exactly 8 bits per parameter - Near-lossless quantization — perplexity almost identical to F16 - About 55-60% the size of F16 - Slower than Q4 on CPU, but faster on GPU due to better hardware support - Best for: when quality is paramount and you have the RAM/VRAM budget My recommendation: Start with Q4_K_M. The quality difference is negligible for conversational tasks, and you get significantly faster inference and lower memory usage. Only upgrade to Q8_0 if you're doing precision-sensitive tasks like code generation benchmarks or fine-tuning evaluation.

Q2_K is the most aggressive quantization and generally not recommended for production use unless you're severely memory-constrained. At 2-bit quantization, the quality degradation becomes noticeable — you'll see increased hallucinations, worse coherence on longer outputs, and degraded performance on reasoning tasks. The perplexity increase can be 5-15% depending on the model architecture. When Q2_K might make sense: - Running a 70B model on hardware that can only fit a 13B at Q4 - Rapid prototyping where you just need the model to "work" loosely - Testing model behavior patterns rather than output quality For anything serious, I'd set Q4_K_S as your minimum floor.