Quantization tradeoffs across quality, context length, and speed are opaque

Summary

Users cannot easily predict whether a larger model at a lower bit-width will outperform a smaller model at a higher bit-width for their actual context length and speed needs. Quantization-aware releases, GGUF variants, GPTQ, and AWQ formats promise different tradeoffs, but there is no trusted per-hardware guide showing expected quality loss, long-context degradation, KV-cache overhead, and tokens/sec for each option. People either run ad hoc benchmarks, rely on vague anecdotes, or pick a quantization that fits but performs poorly. Existing tools rarely model the interaction between quantization, context size, and speed, leaving a central decision unsolved.