Quantization changes more than file size. It changes the memory budget, the kernel path, and whether a model family can run natively at all.

That is why compatibility has to treat quantization as an execution decision, not a cosmetic one. A model in F32 or F16 is not asking the same thing from the runtime as Q8_0, Q6_K, Q5_K_M, Q4_K_M, or IQ4_XS. The supported path depends on the family as well as the tensor type, so the check has to happen before execution starts.

The practical effect shows up fast on limited hardware. A smaller file can still push a machine over the edge if the dequant work or memory layout is wrong for the target backend. The reverse is also true: a heavier format may be the safer choice when you need predictable quality and the model family supports it cleanly.

In day-to-day planning, I find it helps to ask three questions in order: will it fit, will it execute on the native path, and what does the decode path cost once the model is loaded? That keeps the conversation grounded in what the machine can actually do instead of what the filename suggests.

Which quantization choice do you end up optimizing for most often on real hardware: fit, quality, or decode speed?

Engineering fact: Lizard's compatibility layer treats quantization as an execution capability and memory decision; supported native formats include F32, F16, BF16, Q8_0, Q6_K, Q5_K_M, Q4_K_M, and IQ4_XS with family-specific constraints.

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