./main -m ggml-model-q4-0.bin -p "Explain quantum computing" -n 256 Use the convert.py script from the latest llama.cpp to re-package the tensors into GGUF without re-quantizing:
: Q4_0 is the "sweet spot" because it fits perfectly into the L3 cache and RAM bandwidth of most consumer CPUs. It achieves roughly 80-85% of the original model's accuracy for 15% of the memory footprint. Moving to Q8_0 gains only 5% accuracy but doubles memory use; moving to Q2_K halves memory but destroys reasoning. 4. The Successor: Why GGUF replaced GGML (But Q4_0 Persists) Technically, the .ggml format is deprecated. The community has moved to GGUF (GGML Universal Format). The modern equivalent file is model-q4_K_M.gguf . ggml-model-q4-0.bin
While the future belongs to richer formats like GGUF and smarter quantizations like q4_K_M , the humble q4_0 binary will remain the baseline—the "C programming language" of local LLMs: simple, memory-efficient, and fast enough to get the job done. If you see this file, you are looking at the workhorse that made local AI possible. The modern equivalent file is model-q4_K_M