Flux.1, the image model from Black Forest Labs, changed the conversation about what a "good" AI image rig looks like in Nigeria. For a couple of years, an 8GB or 12GB card felt like enough for serious work. Flux.1 quietly raised the bar. It is a much larger model than what most people were running before, and that single fact reshapes every hardware decision around it — from which used GPU to hunt for in the Lagos market to whether your inverter can hold the load.
If you have been generating with SDXL, you already understand the basics, and our Stable Diffusion XL hardware deep dive is the right place to anchor that comparison. The short version is that Flux.1 asks for substantially more memory. If you are still working out the fundamentals, how much GPU VRAM you actually need in 2026 is worth reading first, because VRAM is the whole story here.
Why Flux.1 Is So Much Heavier Than SDXL
The core message of this guide is simple: Flux.1 is a big model. It carries around 12 billion parameters, which makes it dramatically more VRAM-hungry than SDXL. Where SDXL felt comfortable on a 12GB card, Flux.1 in full precision wants far more breathing room. This is not a tuning quirk you can sidestep — it is the size of the model itself.
The practical consequence is that 24GB cards have effectively become the new comfortable floor for serious AI image work. That is a meaningful shift. The card that was once an enthusiast luxury is now the natural home for anyone who wants to run Flux.1 the way it was designed to run, without fighting memory limits at every step.
The VRAM Reality: 24GB Is the Comfortable Home
The full-precision Flux.1 Dev model — running in FP16 or BF16 — wants roughly 24GB of VRAM to sit comfortably and run unquantised. That figure is why 24GB cards are the natural answer. The two obvious candidates are the RTX 3090 and the RTX 4090, both of which carry 24GB.
For most Nigerian builders, the value route is clear. A used RTX 3090's 24GB is the most affordable way to reach that comfortable tier, and the second-hand market makes it realistic. Our look at the used GPU market, RTX 30 versus 40 in Nigeria covers what to inspect and what fair pricing looks like. The 3090 runs hotter and draws more power than newer cards, but its 24GB makes it punch well above its price for Flux work.
The Lower-VRAM Path: Quantisation and FP8
Here is the good news for anyone who cannot reach a 24GB card today: Flux.1 is genuinely runnable on mid-range hardware now, just with compromises. Quantised and reduced-precision versions shrink the model's footprint considerably. FP8 versions, and GGUF-style quantised Flux builds, bring the memory requirement down enough that a 12GB or 16GB card can run it.
A card like the RTX 4070 Super with 12GB can handle quantised Flux.1, which would be impossible with the full-precision model. The trade-off is real, though. Quantisation costs you some speed, and depending on how aggressively you push it, a little quality. If you want to understand exactly what is happening when a model is shrunk this way, quantisation explained for local AI models walks through the mechanics. The headline is that it is a tool for fitting big models onto smaller cards, not a free lunch.
Dev Versus Schnell, and How Compute Plays In
Flux.1 ships in more than one flavour, and the distinction matters for hardware planning.
- Flux.1 Dev — the higher-quality, heavier variant. This is what most people mean when they talk about Flux image quality, and it is the more demanding one to run.
- Flux.1 Schnell — the faster variant, designed to produce results in fewer steps. Schnell is lighter on time, though its memory appetite is broadly similar to Dev. It is the sensible choice when throughput matters more than squeezing out the last bit of quality.
Beyond which variant you pick, raw compute still counts. A stronger GPU generates faster, full stop. But because Flux.1 is so much larger than SDXL, every image takes longer on the same card than an SDXL image would. That is the cost of the bigger model, and it is felt most on the lower tiers where you are already leaning on quantisation to fit at all.
Running Flux: ComfyUI, System RAM and Storage
In practice, most people run Flux.1 — especially the quantised builds — through ComfyUI, which handles model loading, offloading, and the node-based workflows these models thrive on. If you are setting that side up, our ComfyUI workflow hardware guide covers the surrounding build. ComfyUI's offloading support is part of why mid-range cards can run Flux at all: it can shuttle parts of the model between VRAM and system memory when the GPU alone cannot hold everything.
That makes the rest of the system matter more than usual:
- System RAM — 32GB is the sensible target. Offloading leans on system memory, and 16GB leaves you cramped the moment Flux starts spilling out of VRAM.
- Storage — a fast NVMe SSD is not optional. Flux model files are large, well over 10GB each, and loading them from a slow drive turns every model switch into a wait.
- The GPU — still the centre of gravity. Everything else supports it.
The VRAM Ladder and Rough Naira Tiers
Here is how the tiers shake out for a Nigerian builder, framed by VRAM:
- 12GB–16GB (quantised, compromised) — cards like the RTX 4070 Super 12GB run quantised or FP8 Flux. Workable and increasingly popular, but slower and slightly compromised. Expect roughly ₦650,000 to ₦1,000,000 for the GPU depending on model and condition. A good entry into Flux without the full outlay.
- 24GB (comfortable, full precision) — a used RTX 3090 is the value champion here, often landing in the ₦1,000,000 to ₦1,400,000 range, while a new or used RTX 4090 sits well above that. This is the tier that runs full-precision Flux.1 Dev without compromise.
If you are unsure where these cards sit relative to one another, GPU tiers explained, entry to high-end gives the wider map. Naira prices move with the exchange rate and import conditions, so treat these as orientation rather than fixed quotes.
NEPA, Power Draw and Protecting the Build
A 24GB card is a serious power draw, and that has direct implications when NEPA is unreliable. The RTX 3090 in particular is power-hungry, and long Flux generation sessions keep the card under sustained load for far longer than a quick SDXL render would. This is not a setup you want exposed to sudden cuts.
A capable UPS or inverter is essential, both to ride through brief outages and to protect expensive hardware from the dirty power and voltage swings common on the grid. Size your UPS for the real draw of a 24GB card under load, not its idle figure, and give yourself enough runtime to save work and shut down cleanly when the power drops mid-generation.
Frequently Asked Questions
Can I run Flux.1 on a 12GB card? Yes, but only the quantised or FP8 versions. The full-precision Flux.1 Dev model wants around 24GB, so on a 12GB card like the 4070 Super you will run a reduced-precision build through ComfyUI, accepting some loss of speed and a little quality in exchange for getting Flux running at all.
Is a used RTX 3090 a good buy for Flux in Nigeria? For Flux specifically, it is arguably the best value on the market. Its 24GB lands you in the comfortable full-precision tier at a fraction of a 4090's cost. The catch is power draw and heat, so budget for a solid UPS and good cooling.
Why does Flux take longer per image than SDXL on the same card? Because Flux.1 is a far larger model — around 12 billion parameters versus SDXL's much smaller count. More parameters mean more computation for every image, so even on identical hardware each Flux generation simply takes longer.
The One Thing to Remember
Flux.1 made 24GB the new comfortable floor for serious AI image work. You can absolutely run it on a 12GB or 16GB card with quantisation, and many Nigerian builders do — but if you want the model at full quality without fighting memory at every turn, a 24GB card, most affordably a used RTX 3090, is the home it was built for.
Ready to spec a machine for Flux.1? Build your ideal setup with our configurator, or contact us to talk through the right VRAM tier and power protection for your workflow and budget.