Q8_0 Quantization

Q8_0 is llama.cpp's simplest 8-bit GGUF quantization: weights in INT8, one FP16 scale per 32-element block, no zero-point. Each parameter takes about 8.5 bits including the scale.

Q8_0 is the "near-lossless" tier — perplexity is typically within 0.01 of FP16 on standard benchmarks. The cost is size: a 7B model is ~7.6 GB and a 70B is ~75 GB, only ~46% smaller than FP16. For most local-AI hardware, Q8_0 is overkill; Q5_K_M or Q4_K_M deliver 95%+ of the quality at half the memory.

When to actually pick Q8_0: when you're benchmarking quant impact and need a tight upper bound, or when running a model that's already close to your VRAM ceiling and you need every drop of fidelity.