Quick answer: The RTX 4070 Ti Super (16GB) is the best GPU for Flux for most users. Flux needs at least 12GB VRAM to run, and 16GB gives you comfortable headroom for ControlNet and higher resolutions.
NVIDIA GeForce RTX 4070 Ti Super
16GB GDDR6X16GB VRAM handles Flux Dev with ControlNet comfortably. Fast enough for creative iteration at ~13 seconds per image without the 4090 price.
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Why Flux is more demanding than SDXL
Flux is a next-generation image model built on a flow-matching architecture that produces sharper images with better prompt adherence than SDXL. The tradeoff is higher hardware requirements across the board:
- Larger model weights — the Flux checkpoint is 23.8GB on disk, more than three times SDXL’s ~7GB, and Schnell is no smaller than Dev
- Higher memory overhead — the transformer-based DiT architecture uses more activation memory during inference
- Slower per-step generation — each diffusion step takes longer compared to SDXL at identical resolution
- Less flexible quantization — FP8 helps, but Flux is more sensitive to precision reduction than SDXL (the successor model addresses this — see our best GPU for Flux.2 guide for the 32B FP8-native rebuild, or our Flux.2 vs Flux.1 hardware comparison if you’re deciding whether the upgrade is worth it)
The practical result: a card that runs SDXL comfortably may struggle with Flux. You need more VRAM and a faster GPU to get usable iteration speeds. If you are still primarily running SDXL and deciding whether to upgrade for Flux, our best GPU for SDXL guide covers SDXL-specific hardware recommendations before you make the jump.
Here’s a quick head-to-head of the two cards this guide recommends most often for Flux — swap in any other GPU to see how it compares:
| Spec | RTX 4070 Ti Super | RTX 4090 |
|---|---|---|
| VRAM | 16GB GDDR6X | 24GB GDDR6X |
| Memory bandwidth | 672 GB/s | 1,008 GB/s |
| SDXL (s/image) | 5s | 3.2s |
| Flux Dev (s/image) | 8.5s | 5.5s |
| 7B LLM (tok/s) | 40 tok/s | 65 tok/s |
| TDP | 285W | 450W |
| Street price | $800 | $2,200 |
Flux Schnell vs Flux Dev — what’s the difference?
Flux comes in two main variants, and the difference between them is time, not memory. Both ship as a single 23.8GB BF16 file — flux1-dev.safetensors and flux1-schnell.safetensors, checked 2026-09-13, are the same size to the tenth of a gigabyte.
Flux Schnell:
- Distilled model designed for fast inference
- Generates quality images in 4–8 steps (vs 20+ for Dev)
- Same 23.8GB of weights as Dev — it is faster per image, not lighter on VRAM
- Great for rapid iteration and prompt exploration
- Slightly lower quality ceiling than Dev
Flux Dev:
- Full guidance-distilled model for highest quality
- Typically run at 20–50 steps for best results
- Same 23.8GB checkpoint; ~12GB once loaded at FP8
- Better for final renders, fine-tuned outputs, and LoRA use
- Required for most ControlNet workflows
It is worth being blunt about what that shared number means, because “Schnell is the lighter one” is repeated everywhere including earlier versions of this page. No consumer card runs either variant at the precision they were released in. 23.8GB does not fit a 24GB card once the text encoder and activations are added, so every local Flux setup you have seen is running a quantized build — FP8 at roughly 12GB is the standard one. Schnell earns its place by needing a quarter of the steps, which is a speed argument, not a VRAM one.
Practical recommendation: Use Schnell for exploration, Dev for final renders. On a 12GB card, run either at FP8 — and pick Schnell because four steps beat twenty on a slow card, not because it fits better.
VRAM requirements table
Figures below are what the pipeline occupies at FP8, the precision consumer cards actually run. At the released BF16 precision the checkpoint alone is 23.8GB and nothing on this list applies.
| Flux Workflow | Minimum VRAM | Recommended | Notes |
|---|---|---|---|
| Flux Schnell (1024×1024), FP8 | 12GB | 16GB | Tight on 12GB, comfortable on 16GB |
| Flux Dev (1024×1024), FP8 | 12GB | 16GB | Same weights as Schnell, more steps |
| Flux Dev + ControlNet | 14GB | 16GB | Single ControlNet depth/pose |
| Flux Dev + 2× ControlNet | 16GB | 24GB | Dual control stack |
| Flux Dev + ControlNet + IP-Adapter | 16GB | 24GB | Full creative control stack |
| Flux LoRA training (small batch) | 16GB | 24GB | Batch 1–2 on 16GB |
| Flux LoRA training (batch 4+) | 24GB | 32GB | Better convergence |
| Flux Dev (1.5K resolution) | 16GB | 24GB | High-res needs headroom |
| Flux Dev (2K resolution) | 24GB | 32GB | 4090 minimum |
Cards with 8GB VRAM cannot run Flux at native resolution without aggressive CPU offloading — expect 5–10 minutes per image, not seconds. 12GB is the practical minimum; 16GB is where Flux actually works well. For a deeper breakdown of VRAM tiers and what each one buys you in Flux, see our how much VRAM for Flux guide.
Generation speed benchmarks
Approximate time per image at 1024×1024, 20 steps, Euler sampler in ComfyUI:
| GPU | VRAM | Flux Schnell (8 steps) | Flux Dev (20 steps) | Flux Dev + ControlNet | Price |
|---|---|---|---|---|---|
| RTX 5090 | 32GB | ~2.5 s/img | ~5.5 s/img | ~7 s/img | ~$4,900+ |
| RTX 4090 | 24GB | ~3.5 s/img | ~7.5 s/img | ~9 s/img | ~$2,200 |
| RTX 5080 | 16GB | ~4.5 s/img | ~9.5 s/img | ~12 s/img | ~$1,400 |
| RTX 5070 Ti | 16GB | ~5.0 s/img | ~11 s/img | ~14 s/img | ~$1,050 |
| RTX 4070 Ti Super | 16GB | ~6.0 s/img | ~13 s/img | ~16 s/img | ~$800 |
| RTX 4060 Ti 16GB | 16GB | ~9.0 s/img | ~19 s/img | ~24 s/img | ~$425 |
| RTX 3060 12GB | 12GB | ~16 s/img | ~28 s/img | ~38 s/img | ~$250 used |
Times approximate for single-image generation. Real-world times vary by sampler, batch size, and system RAM.
The speed gap between the RTX 4060 Ti 16GB and the RTX 4070 Ti Super for Flux Dev is meaningful — 13 seconds vs 19 seconds per image adds up fast over a long creative session. When you’re iterating through 50+ prompts, that’s the difference between an hour and an hour and a half.
Best overall: RTX 4070 Ti Super
The RTX 4070 Ti Super remains the sweet spot for Flux in 2026:
- 16GB VRAM handles Flux Dev with ControlNet without memory pressure
- ~13 seconds per Flux Dev image is fast enough for productive iteration
- ~$800 street price is well below the RTX 5080 ($1,400) and 4090 ($2,200)
- Full ComfyUI, Forge, and SwarmUI compatibility
- Handles Flux LoRA training at batch size 1–2 (slow but functional)
If you are coming from Stable Diffusion and upgrading specifically for Flux, this is the card to buy. For Chroma-specific generation workflows built on the Flux architecture, see our best GPU for Chroma AI guide.
Check NVIDIA GeForce RTX 4070 Ti Super on Amazon→Buy on Shopee SG→Best flagship: RTX 4090
For professional workflows or heavy ControlNet stacking, the RTX 4090 gives you 24GB of VRAM and roughly 1.7x faster generation than the 4070 Ti Super:
- Handles Flux Dev + dual ControlNet + IP-Adapter simultaneously (16–20GB combined)
- Flux LoRA training with batch size 4–6 for better convergence
- High-res Flux generation at 1.5K and 2K without tiling
- Future-proof for upcoming Flux variants and heavier workflows
Best budget: RTX 4060 Ti 16GB
At ~$425, the RTX 4060 Ti 16GB is the cheapest new card that runs Flux without constant offloading. See our RTX 4060 Ti Flux capability deep-dive for exactly what workflows fit and which hit limits:
- 16GB VRAM means Flux Dev actually fits without extreme CPU offloading tricks
- Generation runs ~19 seconds per image — slow but workable
- Good for hobbyists who generate a few dozen images per session
- Not suitable for Flux LoRA training at any meaningful batch size
Flux LoRA training: VRAM requirements
Training custom Flux LoRAs is a different workload than inference. VRAM needs scale with batch size:
| Batch size | Minimum VRAM | Recommended GPU | Notes |
|---|---|---|---|
| 1 | 16GB | RTX 4070 Ti Super | Very slow convergence |
| 2 | 18GB | RTX 4090 | Slow but viable |
| 4 | 22GB | RTX 4090 | Good training dynamics |
| 6–8 | 28–32GB | RTX 5090 | Best convergence |
Flux LoRA training on 16GB is technically possible with batch size 1 and FP8 base weights, but it’s painfully slow and requires careful gradient accumulation. 24GB is the practical minimum for useful Flux LoRA training. For training-specific GPU recommendations beyond Flux, our best GPU for LoRA training guide covers SDXL, SD 1.5, and Flux LoRA workflows in detail.
ComfyUI optimization tips for Flux
These settings significantly improve Flux performance in ComfyUI:
- FP8 checkpoint quantization — load Flux in FP8 instead of the released BF16 to halve the weights, from 23.8GB to roughly 12GB, with minimal quality loss. This is not an optimization for tight cards, it is the only way Flux runs on a consumer GPU at all. For a deeper look at precision trade-offs, see our best quantization for Stable Diffusion guide.
- Use Flux Schnell for iteration — 4–8 steps instead of 20+ cuts time by 60% during prompt exploration
- Keep ControlNet preprocessors unloaded when not actively using them (ComfyUI node setting)
- Enable model unloading between generations if VRAM is tight
- TAESD VAE instead of full VAE for preview images — much lower VRAM overhead
- Close Chrome and other GPU-using apps — Flux uses nearly all available VRAM and even browser GPU acceleration competes
If you’re coming from ComfyUI workflows with SDXL, note that Flux requires specific nodes (ComfyUI-FluxGuidance, etc.) and the workflow setup is different. If you are also weighing whether to use ComfyUI or Automatic1111 for Flux, our Automatic1111 vs ComfyUI comparison explains which frontend handles Flux VRAM more efficiently.
Not ready to buy hardware? Try cloud GPU first
Renting a GPU to test Flux workflows before buying is smart. RunPod offers RTX 4090 instances for ~$0.50/hr — enough to run an entire Flux session before committing $800+.
Try RunPod — rent an RTX 4090 for Flux testing→Which GPU should YOU buy for Flux?
- You generate Flux images casually (a few dozen per session, no ControlNet): The RTX 4060 Ti 16GB at $425 runs Flux Dev without offloading. Generation is slow at ~19s but the model fits and the price is right.
- You generate frequently and want fast iteration: The RTX 4070 Ti Super at ~$800 is the sweet spot. 16GB handles all Flux workflows, and 13s per image is fast enough for creative work.
- You use Flux with ControlNet, IP-Adapter, or multiple LoRAs stacked: You need 24GB. The RTX 4090 prevents out-of-memory errors when combining multiple control modules.
- You train custom Flux LoRAs: 16GB works only at batch size 1 with FP8 quantization — slow and limiting. The RTX 4090 at 24GB makes Flux LoRA training practical. The RTX 5090 at 32GB makes it comfortable.
- You want maximum future-proofing: RTX 5090 at 32GB handles every current and near-future Flux variant, including multi-ControlNet at 2K resolution.
Common mistakes to avoid
- Buying an 8GB GPU expecting it to run Flux. Flux cannot run at native resolution on 8GB without CPU offloading that takes 5–10 minutes per image. 12GB is the real minimum, 16GB is recommended.
- Using Flux Dev for every generation. Flux Schnell produces excellent results in 4–8 steps using a fraction of the generation time. Use Schnell for iteration and Dev for final outputs.
- Treating FP8 as an optimization you can skip. FP8 halves the 23.8GB checkpoint to roughly 12GB. There is no consumer card that holds the BF16 release, so on a 12GB card this is the difference between Flux fitting or not.
- Expecting AMD GPUs to work well with Flux. The Flux ecosystem’s optimized ComfyUI nodes and ControlNet extensions are built around NVIDIA CUDA. AMD ROCm support is inconsistent.
Final verdict
| Budget | GPU | Flux capability |
|---|---|---|
| ~$250 used | RTX 3060 12GB | FP8 required, both variants, slow |
| ~$425 | RTX 4060 Ti 16GB | Full Flux Dev, single ControlNet, slow |
| ~$800 | RTX 4070 Ti Super | Full Flux Dev + ControlNet, good speed |
| ~$2,200 | RTX 4090 | Dual ControlNet + IP-Adapter, LoRA training |
| ~$4,900+ | RTX 5090 | Everything, 32GB, LoRA at batch 8 |
NVIDIA GeForce RTX 4070 Ti Super
16GB GDDR6XThe 16GB sweet spot for Flux generation. Handles Flux Dev, ControlNet, and single LoRA workflows at speeds fast enough for real creative work.
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For most Flux users, buy the RTX 4070 Ti Super. Only step up to the 4090 if you need training capability, dual ControlNet stacking, or production-level throughput.
Flux is a VRAM-first workload — buy the most VRAM you can afford, then worry about speed.
Frequently asked questions
How much VRAM does Flux need?
Both variants ship as the same 23.8GB file, so neither runs at released precision on a consumer card. At FP8 the pipeline needs about 12GB minimum and 16GB comfortably, for Schnell and Dev alike. With ControlNet, add 2–3GB per active control module. FP8 halves the weights rather than trimming them, which is why it is the default path and not a workaround.
Can a 12GB GPU run Flux?
Yes, but with limitations. A 12GB GPU like the RTX 3060 12GB runs either variant at native resolution once the checkpoint is FP8 — the same 12GB of weights either way, so the choice is about steps rather than fit. Generation is slow (~28 seconds per image) and ControlNet workflows may exceed VRAM. 16GB is the practical sweet spot where Flux Dev runs comfortably without tricks.
Is Flux faster than SDXL?
No — Flux is slower per image than SDXL at equivalent resolution. SDXL generates a 1024px image in roughly 5–9 seconds on an RTX 4070 Ti Super, while Flux Dev takes 13 seconds. However, Flux requires fewer refinement steps to reach quality equivalent to SDXL with refiner, so total time for a high-quality final render can be comparable.
What’s the minimum GPU for Flux LoRA training?
16GB VRAM is the minimum for Flux LoRA training at batch size 1 with FP8 base weights. In practice, 24GB (RTX 4090) is strongly recommended — it allows batch sizes of 4+ which significantly improves training dynamics and convergence speed. Training on 16GB works but is slow and requires careful configuration.
Should I use Flux Schnell or Flux Dev?
Use Flux Schnell for prompt iteration and exploration — it generates quality results in 4–8 steps rather than 20+, saving 60–70% generation time. Use Flux Dev for final renders where you want maximum quality, or when using ControlNet and LoRAs that are calibrated for Flux Dev. A typical workflow uses Schnell to find good prompts, then switches to Dev for final images.