So shrinking that by 6x (from fp16), would be big win for larger models. True, while TurboQuant can also be applied to model weights, it won't save size over q4 compression, but will have better accuracy.
Edits: Better context
I would strongly recommend exploring that option, renting an RTX 5090 for an evening of image generation for a dollar or two is way more fun then trying to jam big models on little cards. Just take some time to create a reasonable, scripted, deployment workflow for when you create a fresh instance.
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I thought the principal consequence of these KV cache optimisations was letting you run more simultaneous inferences on the same model with the same memory. It doesn’t let you store more model. In some sense that puts local LLM usage at a further disadvantage to inference done in a hyperscaler’s data center.