Quick answer: The RTX 4070 Ti Super 16GB is the best GPU for Krea 2 for most people running the Turbo checkpoint — 8-step generation lands around 3-4 seconds at 1024px in FP8. If you plan to train LoRAs against Krea 2 RAW (the 12.9B undistilled model), skip 16GB entirely and go RTX 4090 24GB.
NVIDIA GeForce RTX 4070 Ti Super
16GB GDDR6X16GB VRAM runs Krea 2 Turbo in FP8 with room for one ControlNet. Around 3-4s per 1024px image — fast enough to prompt-iterate in real time.
Affiliate links — we may earn a commission at no extra cost to you. Amazon ships globally; Shopee SG covers Singapore & ASEAN.
Who this is for
This guide is for image-generation enthusiasts and LoRA trainers who watched Krea AI open-source their in-house foundation model on 2026-06-22 and want to run it locally instead of paying for API credits. Krea 2 shipped in two flavors — a 12.9B RAW checkpoint (undistilled, LoRA-training-ready) and an 8-step distilled Turbo variant — and each has a very different hardware profile. It also ranked #1 text-to-image on the independent Artificial Analysis leaderboard at launch — rankings there move as new models land, so check the current standings rather than taking the launch position as permanent — which means the demand curve is real and the “cheapest card that runs it well” question matters more than usual.
If you’re a Flux user asking whether your current rig carries over, mostly yes for Turbo, mostly no for RAW+LoRA. My best GPU for Flux.2 guide gives you the neighboring numbers if you want to run both models on one card. Qwen users, similarly, get the best GPU for Qwen Image breakdown.
RAW vs Turbo: the actual VRAM story
Krea 2 RAW is a 12.9B DiT diffusion transformer. In FP16 the weights alone sit around 26GB before you add the text encoder, VAE, and activation memory — meaning single-consumer-GPU FP16 is only comfortable on the RTX 5090. FP8 drops the base to roughly 14GB, which brings 16GB cards into play for inference. LoRA training against RAW is the interesting case: you need optimizer state plus gradients on the LoRA layers, which pushes practical VRAM to ~22-24GB even with 8-bit AdamW.
Krea 2 Turbo is the distilled variant tuned for 8-step inference. Same architecture, same tokenizer, but the sampling schedule collapses to a fraction of the compute. On a flagship it hits the ~2-second mark that VentureBeat quoted in the Krea release coverage. In FP8, Turbo fits inside 12GB, which is genuinely new territory for a top-ranked foundation image model.
| Workload | FP16 VRAM | FP8 VRAM | Q4 (GGUF-style) |
|---|---|---|---|
| Krea 2 Turbo inference (1024px, 8 steps) | ~26GB | ~11-12GB | ~8GB |
| Krea 2 Turbo + 1 ControlNet | ~28GB | ~14GB | ~10GB |
| Krea 2 RAW inference (1024px, 28-40 steps) | ~26GB | ~14GB | ~10GB |
| Krea 2 RAW + LoRA training (batch 1) | ~34GB+ | ~22-24GB | not recommended |
| Krea 2 RAW + full ControlNet stack | ~32GB | ~18-20GB | ~14GB |
For a broader mental model of how VRAM budgets map to diffusion workloads generally, my how much VRAM for Stable Diffusion explainer walks through the base + text-encoder + activation math that produces these numbers.
GPU ranking for Krea 2 in 2026
Approximate ComfyUI times with the Diffusers Krea 2 pipeline (which landed July 2026), Euler-A sampler, no ControlNet, 1024×1024 output. Turbo is 8 steps, RAW is 28 steps.
| GPU | VRAM | Turbo (8 step) | RAW (28 step) | Price |
|---|---|---|---|---|
| RTX 5090 | 32GB | ~1.8s | ~6s | ~$4,900 |
| RTX 4090 | 24GB | ~2.2s | ~8s | ~$2,200 |
| RTX 4070 Ti Super | 16GB | ~3.5s | ~13s (FP8 only) | ~$800 |
| RTX 5070 Ti | 16GB | ~3.1s | ~11s (FP8 only) | ~$1,050 |
| RTX 3090 (used) | 24GB | ~4.0s | ~14s | ~$820 used |
| RTX 4060 Ti 16GB | 16GB | ~7s | ~24s (FP8 only) | ~$425 |
The three columns you actually care about are VRAM, Turbo speed, and whether the card can touch RAW at all. Anything below 16GB is Turbo-only territory. Anything below 24GB is inference-only for RAW — no LoRA training.
Check NVIDIA GeForce RTX 4070 Ti Super on Amazon→Buy on Shopee SG→Which GPU should YOU buy for Krea 2?
- You’ll only run Krea 2 Turbo, no LoRA training: RTX 4070 Ti Super at ~$800. 16GB is comfortable in FP8, and 3-4 second gens are inside the “I can prompt-iterate without losing focus” window.
- You want faster Turbo and occasional ControlNet stacking: RTX 5070 Ti at ~$1,050. Same 16GB but Blackwell tensor cores knock about 15% off gen time versus Ada.
- You’ll run Krea 2 RAW inference (not training): RTX 3090 24GB used at ~$820 is genuinely the value pick. FP16 RAW fits with headroom, LoRA loading works, and used flagship VRAM beats new mid-range VRAM every time for this workload. The best GPU for ComfyUI guide covers the node graph you’ll want.
- You’ll train LoRAs against Krea 2 RAW: RTX 4090 24GB is the floor. Optimizer state alone eats ~4-6GB on top of the FP8 base, and 16GB cards OOM the moment you unfreeze more than the smallest LoRA rank. My best GPU for LoRA training guide has the batch-size and gradient-accumulation math if you want to push rank higher.
- You’re a working artist who needs the fastest possible iteration: RTX 5090 at ~$4,900. Sub-2-second Turbo means the render never becomes the bottleneck — you become the bottleneck. This is the “money is worth less than time” pick.
- You’re on a budget and want to try it: RTX 4060 Ti 16GB at ~$425 runs Turbo in FP8. 7 seconds per image is slow for real-time prompt work but perfectly fine for batch generation.
The contrarian take: skip RAW entirely if you’re not training LoRAs
Here’s the thing most launch-week guides won’t tell you: if you’re not planning to train custom LoRAs, the RAW checkpoint is mostly a research artifact for you. Turbo output quality is within a few percentage points of RAW on the Artificial Analysis leaderboard, ships at ~4x the speed, and fits in half the VRAM. The reason RAW exists as a public download is that Krea open-sourced the undistilled weights so the community can build on them — LoRAs, DreamBooth, fine-tunes. If your workflow is prompt-in, image-out, then RAW’s larger VRAM footprint is buying you nothing you’ll perceive in the output.
This changes the buying calculus. A 16GB card is the right pick for probably 80% of Krea 2 users. The temptation is to spec for RAW “just in case,” but if you’re honest about your workflow — do you actually train LoRAs, or do you tell yourself you might? — the 16GB tier delivers the model at Turbo quality with real-time iteration, and the $700-900 you save funds a used display, an SSD upgrade, or another year of RunPod credits for the occasional RAW experiment.
Common mistakes with Krea 2
- Buying an 8GB card for Turbo. Turbo runs in ~11-12GB FP8 with headroom, not 8. RTX 4060 8GB, RTX 3070, and similar will OOM the moment you add the text encoder or bump resolution to 1152px. 12GB is the floor and 16GB is where it stops being anxious.
- Assuming the “12GB is fine for Flux” tier maps to Krea 2. Flux.1 Dev at 12B and Krea 2 at 12.9B look similar on paper, but Krea 2’s text encoder and VAE are chunkier — the practical VRAM budget is 15-20% higher. Cards that scraped by on Flux.1 (RTX 4070 12GB, RTX 3060 12GB) will need Q4 quantization for Krea 2 Turbo and will still feel tight.
- Trying to train Krea 2 RAW LoRAs on a 16GB card at FP16. Optimizer state plus gradients plus activations pushes total VRAM past 30GB even at LoRA rank 16. You will OOM. Options: drop to 8-bit AdamW + FP8 base (still ~22GB, so 24GB is the real floor), or move training to cloud. Do not buy a 16GB card expecting to train against RAW — this is the most expensive misread in the guide.
- Ignoring the diffusers pipeline entirely and hand-porting from the Krea reference code. The July 2026 Diffusers integration handles FP8 casting, sampling scheduler, and text encoder pairing correctly. Every “why does my Krea 2 output look worse than the leaderboard samples” thread I’ve seen traces back to a custom loader that skipped one of these steps.
Final verdict
| Budget | GPU | Krea 2 capability |
|---|---|---|
| $4,900 | RTX 5090 32GB | RAW FP16, RAW LoRA training rank 128, Turbo sub-2s |
| $2,200 | RTX 4090 24GB | RAW FP16 comfortable, LoRA training rank 32-64 |
| $820 used | RTX 3090 24GB | RAW FP16 inference, LoRA training at low rank |
| $800 | RTX 4070 Ti Super 16GB | Turbo in FP8 with ControlNet, RAW inference in FP8 |
| $425 | RTX 4060 Ti 16GB | Turbo in FP8 (slow), RAW inference in FP8 (tight) |
NVIDIA GeForce RTX 4070 Ti Super
16GB GDDR6XKrea 2 Turbo runs comfortably in FP8 on 16GB with ~3-4s gens. Only step up to a 24GB card if you're specifically training LoRAs against the RAW checkpoint.
Affiliate links — we may earn a commission at no extra cost to you. Amazon ships globally; Shopee SG covers Singapore & ASEAN.
If you’re running Krea 2 Turbo, buy the RTX 4070 Ti Super 16GB; if you’re training LoRAs against RAW, buy the RTX 4090 24GB — everything else is a compromise on one axis or the other.
Frequently asked questions
How much VRAM do I need for Krea 2 Turbo?
Roughly 12GB in FP8 for base 1024px inference; 14GB comfortably with a single ControlNet. 16GB is the practical floor if you want headroom for higher resolutions or stacking control modules.
How much VRAM do I need for Krea 2 RAW?
About 14GB in FP8 for inference-only workflows and around 22-24GB for LoRA training with 8-bit AdamW. Full-precision FP16 inference wants ~26GB, so 24GB flagship cards are the practical minimum.
Can I train Krea 2 LoRAs on a 16GB GPU?
Not comfortably. Optimizer state and gradients push total VRAM past what 16GB cards can hold even at LoRA rank 16. A 24GB card such as the RTX 4090 or a used RTX 3090 is the realistic floor for local LoRA training against Krea 2 RAW.
Is Krea 2 Turbo actually faster than Flux.2?
Yes, meaningfully so on the same hardware. Turbo’s 8-step distillation lands around 2 seconds on flagship cards versus roughly 6-11 seconds for Flux.2 depending on precision, though Flux.2 tends to have stronger text rendering while Krea 2 leads on the Artificial Analysis quality leaderboard at launch.
Do I need a 24GB card if I only run Krea 2 Turbo?
No. 16GB in FP8 is enough for Turbo including one ControlNet. 24GB only becomes necessary if you also want to run the RAW checkpoint at higher precision or train LoRAs — for pure Turbo inference, 16GB is the right tier.