Quantization, distillation, pruning: making models fit
Three ways to shrink an LLM. Quantization (Q2-Q8 with K-quants in GGUF), distillation (teacher to student), pruning. Why Q4_K_M is the community default and what each lever costs.
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Posts on AI engineering, LLM systems, and software development.
Three ways to shrink an LLM. Quantization (Q2-Q8 with K-quants in GGUF), distillation (teacher to student), pruning. Why Q4_K_M is the community default and what each lever costs.
Parameters, B, dense vs MoE, base vs instruct, tokens, context window, chat template, GGUF, quantization suffixes. After this post you can read any HuggingFace model card.