Checkpoint: Reading a Fit Result Without Being Misled

A row in llmfit is useful only when its parts are read in the right order. The row connects a machine’s memory topology, a model’s metadata, a chosen context and quantization, an execution path, and a headroom verdict. The practical judgment is not “is this score high?” but “what configuration is being evaluated, where will it run, how tightly does it fit, and what remains uncertain?”

The earlier ideas now form one interpretation method. Hardware tells us which pools exist. Model metadata tells us what must be placed into them. Memory accounting turns that configuration into a demand. Path selection says where the demand will be carried. The fit level describes the remaining room. Speed, quality, context, and measurement confidence add further evidence, but they should not replace that foundation.

Start with the configuration, not the headline label

The first question is: what exactly is being evaluated? A model name is not enough. The same model can be examined at different weight quantizations, context settings, or KV-cache representations. Those choices change the memory requirement. A fit row is therefore a statement about a model under a configuration, not a permanent property attached to the name.

The second question is: which machine and memory topology supply the resources? On a discrete-GPU system, the relevant GPU path is graded against VRAM, while an offload or CPU-only path uses system RAM. On a unified-memory system, the CPU and GPU share one pool, so the analysis does not invent a separate RAM spill destination. With multiple GPUs, llmfit may use aggregate VRAM for a supported split, but that is a capacity assumption tied to the execution path, not a guarantee that every runtime scales across the cards.

Only after those two questions should the reader examine memory. The estimated demand includes the selected weight representation, context-dependent KV state, and fixed runtime overhead. If the model’s architecture metadata is complete, the KV term can be calculated more precisely. If it is incomplete, the result may rely on a coarser approximation. The number is still useful for comparison, but the missing information belongs in the reader’s confidence judgment.

The third question is: what did the analysis do with the available pool? A model that remains fully on the GPU has a different run mode from one that places some work in system RAM. An MoE model may use active experts in VRAM and inactive experts in RAM. A CPU-only model may fit with room to spare but cannot receive the strongest GPU-resident verdict. A fit label without its run mode hides the main operational consequence of the row.

The order can be summarized as a reading path:

flowchart TD A[Hardware topology] --> B[Model and configuration] B --> C[Memory and context] C --> D[Run path and headroom] D --> E[Speed and confidence caveat]

This order prevents the score from becoming the starting point. A composite score is downstream of the other decisions. If the row uses an unexpectedly low quantization, a constrained usable context, or a CPU-offload path, the score must be interpreted in that light.