Will it fit
on your card?

About 5.1 GB at Q4_K_M with a 4K context window — 4.2 GB of weights, 0.5 GB of KV cache and a 0.5 GB activation buffer, measured on WizardLM-2 7B (7B). Every 8 GB card in this index clears that with room left. The figure rises with context, not with how hard you use the model: the same file at 32K needs about 9.0 GB, because the KV cache grows to 4.0 GB.

At 4 bits and short context, yes — Mistral Small 24B Instruct at AWQ INT4 needs about 13.2 GB of 16 GB, leaving 2.8 GB. The level matters more than the parameter count: the same model at Q4_K_M is 15.9 GB, which is 99% of the card — it loads on a card with nothing else on it and has no margin for a longer window. A 16 GB card runs 64 of the 112 models here comfortably.

At Q4_K_M the median published perplexity loss across the 79 models here that report one is 2.9% — 97.1% retained — with the range running 1.4% to 5.2%. Two caveats that matter more than the median: smaller models lose more than larger ones at the same level, and perplexity is a language-modelling measure, not a measure of whether the model still does your task. Models without a published figure are shown as a dash here rather than assumed to be lossless.

GGUF unless you are running a server. GGUF runs on anything — CPU, NVIDIA, AMD, Apple — and 112 of the 112 models here ship it, against 53 for AWQ. AWQ's case is throughput under vLLM with batched requests, on an NVIDIA card or one of the Radeon cards vLLM's ROCm build supports; for one person talking to one model, it buys you nothing GGUF does not already do. 53 models here ship both, which is the only set where the two can be compared on the same weights.