FP16 is the standard for local diffusion models; quantising Flux.1 Dev from FP16 (-23.8GB) to FP8 or GGUF reduces VRAM to fit 12-16GB GPUs at modest quality cost.
A modern AI art model like Flux.1 Dev is enormous, and in its native form it simply will not fit on a mid-range graphics card. The reason you can still run it on a 12GB or 16GB GPU comes down to one idea: precision. By storing the model's numbers in a smaller format, quantisation shrinks the memory it needs, trading a sliver of image quality for the ability to actually load it. Understanding FP16 versus quantised versions is the difference between staring at an out-of-memory error and generating images locally.
Flux.1 Dev in full FP16 precision needs around 24GB of VRAM, which rules out most consumer cards. Quantising it to FP8 roughly halves that to about 12GB, and GGUF formats can take it lower still, with Q8 at near-identical quality and Q4 fitting into 6 to 8GB. For a 12GB card, GGUF Q8 is the sweet spot; for 16GB, FP8 or Q8 runs comfortably at near-full quality.
Every weight in an AI model is a number, and you can store that number with more or fewer bits. FP16 uses sixteen bits per value and is the standard full-precision format for local diffusion models. It keeps the model as the creators trained it, but it is also the heaviest, which is why Flux.1 Dev in FP16 demands roughly 24GB of VRAM just to hold the transformer weights.
Quantisation reduces the bits per value. Drop to FP8 and you halve the memory footprint to around 12GB. Go further with GGUF formats, which can use even fewer bits per weight, and the model shrinks again. The catch is that fewer bits means slightly less precise numbers, which can show up as small differences in the final image.
The practical numbers are what matter when you are matching a model to your card. FP16 sits near 23.8GB, which needs a 24GB GPU. FP8 lands around 11.9GB, comfortable on a 16GB card. GGUF gives you a ladder of options: Q8 keeps quality virtually identical to full precision and suits a 12GB card well, Q5 or Q6 also fit 12GB, and the more aggressive Q4 squeezes down to roughly 6.8GB for an 8GB card.
The quality trade-off is gentler than people expect at the top of that ladder. Q8 is, for most eyes, indistinguishable from FP16. The losses only become noticeable as you push down toward Q4, where fine detail and prompt adherence start to soften. So the rule of thumb is simple: use the highest precision your VRAM can hold, and only step down when you must.
For a 12GB card, GGUF Q8 is the clear recommendation. It keeps quality essentially intact while fitting the available memory, often paired with a lighter FP8 text encoder to save a little extra room. For a 16GB card, you have headroom for either FP8 or GGUF Q8, both of which run at near-full quality with comfortable margin. On an 8GB card you are into Q4 territory, which works but asks you to accept a visible quality cost for the privilege of running locally at all.
If you are building or buying a machine for this kind of work, VRAM is the spec that decides what you can run, far more than raw clock speed. The AI-ready PCs at Evetech are configured with local generation in mind, and when you are shopping purely for the graphics card, the most popular GPUs at Evetech make it easy to see which cards land in the 12GB and 16GB band that suits Flux.
Only slightly, and mostly at aggressive levels. At Q8 the difference from full FP16 is negligible for most images. Quality only starts to visibly soften as you push down toward Q4 to fit smaller cards.
FP8 is a single eight-bit floating-point format that roughly halves memory use. GGUF is a family of quantisation formats offering several levels, from high-quality Q8 down to memory-saving Q4, giving you finer control over the size-versus-quality balance.
Yes. GGUF Q8 is the recommended choice for 12GB, keeping quality close to full precision while fitting the available VRAM, often with a lighter text encoder to free up a little more room.
FP16 stores every model weight at full sixteen-bit precision, which is the heaviest format. Flux.1 Dev has a large number of weights, so at FP16 it adds up to around 24GB, more than most consumer cards have.
Want a machine that can actually load these models locally? Compare options on the AI-ready PC range at Evetech and choose the VRAM tier that matches the precision you plan to run.
Only slightly, and mostly at aggressive levels. At Q8 the difference from full FP16 is negligible for most images. Quality only starts to visibly soften as you push down toward Q4 to fit smaller cards.
FP8 is a single eight-bit floating-point format that roughly halves memory use. GGUF is a family of quantisation formats offering several levels, from high-quality Q8 down to memory-saving Q4, giving you finer control over the size-versus-quality balance.
Yes. GGUF Q8 is the recommended choice for 12GB, keeping quality close to full precision while fitting the available VRAM, often with a lighter text encoder to free up a little more room.
FP16 stores every model weight at full sixteen-bit precision, which is the heaviest format. Flux.1 Dev has a large number of weights, so at FP16 it adds up to around 24GB, more than most consumer cards have.
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