Freemium: Quantization Tradeoffs Exposed

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Welcome to today’s edition of Business Analytics Review!

Most teams still treat 4-bit models as interchangeable commodities. Grab the first GGUF, GPTQ, or AWQ checkpoint on Hugging Face, drop it into the nearest runner, and declare victory on VRAM. The reality is sharper. The same Llama or Qwen weights quantized three different ways can swing code-generation accuracy by nearly 10 points, alter tokens-per-second by 20-30 percent on identical hardware, and decide whether the model even fits a production batch size. Weight-only post-training methods dominate because they need no retraining, yet the hidden differences in how each method handles outliers, calibration, and runtime kernels create silent failure modes that only surface under real load.