Overview
This series explains how llmfit turns a machine profile and model record into a conditional local-inference recommendation. It connects memory pools, quantization, KV cache, usable context, execution paths, MoE placement, decode and prefill estimates, ranking, runtime compatibility, and benchmarks. The result shows what fits, how it may run, and how confidently to act without treating a score or estimate as a universal guarantee.
Learning outcomes
- A fit result belongs to a model configuration, memory topology, execution path, and context—not to a model name alone.
- Weights, KV cache, and runtime overhead jointly determine memory demand, while usable context can be below the native window.
- Decode, prefill, fit, quality, and context are separate dimensions that ranking combines according to the workload.
- Measured results are strongest for matching hardware and configuration; estimates remain conditional rather than universal guarantees.
Keywords
Preview every chapter · Unlock full text, audio, and video from chapter 4
PlansFAQ
What does “fit” mean in llmfit?
In llmfit, “fit” means a specific model configuration has a feasible memory path and headroom on the analyzed machine; it does not guarantee speed, task quality, or universal suitability.
Why is parameter count not enough in llmfit?
In llmfit, parameter count alone is insufficient because weight format, KV-cache growth, memory topology, runtime compatibility, context, and workload needs can change the practical choice.
How does llmfit distinguish estimated and measured speed?
llmfit labels throughput by provenance: local and matching community measurements outrank calibrated formulas, which outrank untouched estimates; unsupported runtimes receive no invented number.
Can a model fit and still be the wrong choice?
Yes. A model can fit yet be too slow, have too little usable context, lack task alignment, or require a runtime the intended environment cannot support.