No trustworthy way to pick which local model + quantization actually fits your hardware — users resort to asking strangers on forums

Summary

Local-AI users with a known GPU/RAM budget still have no reliable way to answer the most basic question: which model at which quantization (GGUF Q4_K_M vs Q5_K_M vs GPTQ/AWQ) and which context length will actually run well on MY machine? The source is a plain 'what are the go-to 4B and 9B models to run locally?' plea for community opinions — evidence that today the decision is made by anecdote, Reddit threads and trial-and-error rather than by any hardware-aware tool. The cost is real: users burn hours and bandwidth downloading multi-GB weights that don't fit or run too slowly, then start over. Existing model hubs list files and sizes but never map them to a specific VRAM/RAM budget, never account for KV-cache growth at longer context, and never trade off quality loss vs speed. Because no product closes this gap end-to-end, the same question is re-asked hundreds of times a day, and users would gladly pay for a definitive 'this is the best fit for your rig' answer.