Cross-posted from Best GPU for AI — visit the original for our VRAM calculator, GPU comparison table, and current Amazon pricing.
Quick answer: The RTX 5080 16GB is the best GPU for Flux.2 for most people in 2026. Black Forest Labs released Flux.2 in May 2026 at 32B parameters — roughly 2.7x larger than Flux.1 Dev — but NVIDIA's FP8 optimization (per NVIDIA's May 2026 RTX AI Garage announcement) drops VRAM needs by around 40%, which is what makes 16GB consumer cards viable at all.
See the recommended pick on the original guide
Who this is for
This guide is for image-generation users deciding which GPU to buy specifically for Flux.2 — the new 32B-parameter model that launched in May 2026. If you already own a 16GB card that struggled with Flux.1 Dev in FP16, the FP8 path changes the calculus completely. If you can already run Flux.1 fine and you're wondering whether to upgrade, the honest answer at the bottom may surprise you.
The FP8 story: why Flux.2 is more accessible than Flux.1
Flux.1 Dev was a 12B-parameter model that, in FP16, wanted around 24GB to run cleanly with ControlNet. Flux.2 ships at 32B — almost three times the parameter count — so in raw FP16 it sits around 28GB and effectively locks out everything below an RTX 5090.
Then NVIDIA published their RTX AI Garage post and ComfyUI nodes that ship a Flux.2 FP8 checkpoint tuned for Blackwell and Ada tensor cores. FP8 cuts memory by roughly 40% versus FP16 on this model, with image quality differences that most users won't notice in side-by-side blind tests. The practical result: a 16GB card now fits the larger Flux.2 model in memory with headroom for a single ControlNet.
This is genuinely new. Until May 2026, "best GPU for Flux" guides (including my Flux.1 buyer's guide) said 24GB was the comfortable target. Flux.2 with FP8 changes the floor — if you're not ready to upgrade from Flux.1, that earlier guide still stands on its own.
FP8 vs FP16 VRAM requirements for Flux.2 32B
| Workload | FP16 VRAM | FP8 VRAM | Q4 VRAM |
|---|---|---|---|
| Flux.2 base inference (1024px, 20 steps) | ~28GB | ~16GB | ~10-12GB |
| Flux.2 + 1 ControlNet | ~30GB | ~18GB | ~13GB |
| Flux.2 + 2 ControlNets + IP-Adapter | ~34GB+ | ~22GB | ~16GB |
| Flux.2 at 1.5K resolution | ~32GB | ~20GB | ~14GB |
| Flux.2 LoRA training (batch 1) | ~38GB | ~22GB | not recommended |
A few notes on the numbers above. FP8 is the sweet spot — quality loss is minimal and the savings are real. Q4 (4-bit quantization, GGUF-style) gets you onto a $400 card but you'll see softer detail and weaker prompt adherence, particularly on text rendering and small faces. For an explainer of how these tiers map to actual VRAM choices, see how much VRAM you need for Flux workflows.
VRAM chart available at the original article
Generation time per 1024×1024 image, 20 steps
Approximate ComfyUI times with the official Flux.2 FP8 checkpoint, Euler sampler, no ControlNet:
| GPU | VRAM | Precision used | Time per image | Price |
|---|---|---|---|---|
| RTX 5090 | 32GB | FP16 native | ~6 s | ~$2,000 |
| RTX 4090 | 24GB | FP8 (FP16 tight) | ~9 s | ~$1,600 |
| RTX 5080 | 16GB | FP8 | ~11 s | ~$1,000 |
| RTX 5070 Ti | 16GB | FP8 | ~13 s | ~$750 |
| RTX 4070 Ti Super | 16GB | FP8 | ~16 s | ~$700 |
| RTX 3090 (used) | 24GB | FP8 (FP16 possible) | ~14 s | ~$700 used |
| RTX 4060 Ti 16GB | 16GB | Q4 (FP8 borderline) | ~28 s | ~$400 |
The RTX 5090 is the only consumer card that runs Flux.2 in FP16 comfortably out of the box. Everything else needs FP8, and that's fine — most of these times are inside the "I can keep iterating" zone.
See the recommended pick on the original guide
Which GPU should YOU buy for Flux.2?
- You're running Flux.2 daily, want fast iteration, no LoRA training: RTX 5080 at ~$1,000. 16GB + Blackwell FP8 throughput is the right shape for this workload.
- You need ControlNet stacking, IP-Adapter, or higher resolutions: RTX 4090 at ~$1,600 (24GB) or step up to the RTX 5090. 16GB FP8 starts getting tight when you stack two control modules plus an IP-Adapter.
- You train Flux.2 LoRAs or do serious fine-tuning research: RTX 5090 32GB is the practical floor. For broader training and fine-tuning hardware advice including multi-GPU setups, see my best GPU for AI research guide — it covers the bandwidth and VRAM math for research-grade workloads where Flux.2 is just one of several models in rotation.
- You're on a budget and just want to generate occasional images: RTX 4060 Ti 16GB at ~$400 will run Flux.2 in Q4 quantization. Expect ~28s per image and softer fine detail, but it works.
- You already own an RTX 3090: Keep it. 24GB used at ~$700 still runs Flux.2 in FP8 fine, and FP16 is reachable if you turn off ControlNet. The $1,000 5080 upgrade is mostly a speed gain, not a capability gain.
- You can run Flux.1 Dev comfortably already: Honestly, hold. Flux.2 produces visibly better hands and text, but the prompt-adherence delta is smaller than the marketing suggests. If your Flux.1 workflow is working, this isn't a must-upgrade — wait for prices on the 5080/5090 to settle later in 2026.
For workflow setup once you've picked a card, my best GPU for ComfyUI guide covers node configuration, model loading order, and the FP8 toggle that some people miss on first install.
Common mistakes with Flux.2
-
Loading the FP16 checkpoint by default. ComfyUI's Flux.2 node defaults vary by version — if your 16GB card is hitting OOM, check that you actually selected the
flux2-dev-fp8weights, notflux2-dev-fp16. This is the #1 issue I see in support threads. - Assuming Flux.2 needs the same setup as Flux.1. The tokenizer, the text encoder pairing, and the recommended sampler all changed. Copy-pasting your Flux.1 ComfyUI workflow will produce broken or weirdly-blurry images. Start from the official Flux.2 example workflow (also flagged in NVIDIA's May 2026 RTX AI Garage announcement).
- Buying a 12GB card for Flux.2. RTX 4070 (non-Ti), RTX 3060 12GB, and similar cards do not have enough VRAM for Flux.2 even in FP8 once you account for ControlNet and the text encoder. 16GB is the real floor.
- Mixing Flux.1 LoRAs with Flux.2. The model architecture changed enough that Flux.1-trained LoRAs do not transfer cleanly. Wait for Flux.2-specific LoRA releases or retrain — and if you do retrain, the AI research GPU recommendations for VRAM-heavy fine-tuning apply directly here.
Final verdict
| Budget | GPU | Flux.2 capability |
|---|---|---|
| ~$400 | RTX 4060 Ti 16GB | Q4 only, slow (~28s), softer detail |
| ~$700 used | RTX 3090 24GB | FP8 comfortable, FP16 possible without ControlNet |
| ~$700 | RTX 4070 Ti Super | FP8 fits, ~16s per image, single ControlNet |
| ~$750 | RTX 5070 Ti | FP8 fits, ~13s, better tensor throughput than 4070 Ti Super |
| ~$1,000 | RTX 5080 16GB | FP8 sweet spot, ~11s, best $/perf for Flux.2 |
| ~$1,600 | RTX 4090 | 24GB unlocks ControlNet stacking and FP16 with care |
| ~$2,000 | RTX 5090 | 32GB FP16 native, LoRA training comfortable, future-proof |
See the recommended pick on the original guide
For most Flux.2 users in 2026, the RTX 5080 is the right buy. Step up to the RTX 4090 or 5090 only if you need 24GB+ for ControlNet stacking, LoRA training, or higher resolutions — otherwise FP8 has genuinely flattened the price curve.
Flux.2 is the first major image model where FP8 quantization isn't a workaround — it's the default path, and that's what brings 32B-parameter quality to $1,000 hardware.
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Read the full guide on Best GPU for AI — includes our VRAM calculator, GPU comparison table, and live pricing.
