Flux.1 Dev needs approximately 23.8GB at FP16, but FP8 quantisation halves that to around 12GB with minimal quality loss, and NF4 squeezes it onto 8GB cards. For full-fidelity local runs, a 24GB card like the RTX 4090 is the comfortable target.
The headline number scares people off Flux.1 Dev before they try it. Yes, the full model is heavy, but the real-world answer to Flux.1 Dev VRAM requirements depends entirely on which precision you run. At full FP16 it wants around 24GB. Quantise to FP8 and it halves. Drop to NF4 or a low GGUF and it squeezes onto an 8GB card. You do not need a top-tier GPU to generate locally, you need to match the model format to the card you own.
Flux.1 Dev needs roughly 24GB at FP16 for no-compromise full quality. FP8 cuts that to around 12 to 13GB on cards like a 16GB GPU, and NF4 or GGUF Q4 fits onto 8GB cards with some quality and speed cost. A 24GB card such as the RTX 4090 is the comfortable target for full-fidelity local runs.
The same model fits very different cards depending on how it is quantised. Here is the practical breakdown.
VRAM is the gate, so pick by how much your card has.
On an 8GB card, Flux runs via NF4 or GGUF Q4 but it is slow, often a minute or more per image. It works, though it is not a joyful daily driver at this tier. Around 12GB is the practical floor for a genuinely good experience, typically using a GGUF build with an FP8 text encoder, landing near 60 to 80 seconds an image. A 16GB card is comfortable, running FP8 or GGUF Q8 at near-full quality in roughly 40 to 55 seconds. A 24GB card removes the compromises entirely: full FP16, any resolution, fast generation.
If you are picking a card specifically for local image generation, lead with VRAM capacity rather than raw gaming benchmarks. The GPU best sellers are a quick way to compare what current cards offer at each memory tier.
The smart move is to run the highest-quality format your VRAM allows. If you have 24GB, run FP16 and forget about it. With 12 to 16GB, FP8 gives you most of the quality at half the memory and is the easiest setup. With 8GB, accept a quantised build and slower speeds, or weigh whether a roomier card is the better long-term buy. Since the model is going nowhere and newer tools keep arriving, VRAM headroom is the investment that keeps paying off. Builders putting together a dedicated machine for this often start from a purpose-specced AI PC rather than retrofitting a gaming rig.
Yes, using NF4 or a GGUF Q4 build, but expect slower generation and some quality loss on certain prompts. It is usable for experimenting; it is not the smooth experience you get from 12GB and up.
Only for uncompromised FP16 at any resolution and full speed. Most people get excellent results on 12 to 16GB cards using FP8 or GGUF Q8, which look near-identical to full precision while using far less memory.
FP8 is the standout choice on 16GB. It is a single file, needs no extra setup, halves the memory load, and produces images most users cannot distinguish from FP16. GGUF Q8 is a close alternative.
Mild quantisation barely affects quality. FP8 and GGUF Q8 stay visually close to FP16. The trade-off only becomes obvious at aggressive levels like NF4 or Q4, where some prompts lose fine detail.
VRAM first. If the model does not fit, it either runs painfully slowly with offloading or not at all. Once you have enough memory for your chosen precision, then faster compute improves how quickly images render.
Match your card to the model and run Flux.1 Dev locally without the guesswork. Compare memory tiers across the graphics cards at Evetech and pick the VRAM that fits your workflow.
Yes, using NF4 or a GGUF Q4 build, but expect slower generation and some quality loss on certain prompts. It is usable for experimenting; it is not the smooth experience you get from 12GB and up.
Only for uncompromised FP16 at any resolution and full speed. Most people get excellent results on 12 to 16GB cards using FP8 or GGUF Q8, which look near-identical to full precision while using far less memory.
FP8 is the standout choice on 16GB. It is a single file, needs no extra setup, halves the memory load, and produces images most users cannot distinguish from FP16. GGUF Q8 is a close alternative.
Mild quantisation barely affects quality. FP8 and GGUF Q8 stay visually close to FP16. The trade-off only becomes obvious at aggressive levels like NF4 or Q4, where some prompts lose fine detail.
VRAM first. If the model does not fit, it either runs painfully slowly with offloading or not at all. Once you have enough memory for your chosen precision, then faster compute improves how quickly images render.
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