Cross-posted from Best GPU for AI — visit the original for our VRAM calculator, GPU comparison table, and current Amazon pricing.
If you're picking one GPU to cover both AI image and AI video generation in 2026, the workload that pulls hardest on your wallet should drive the buy. Flux.2 and HunyuanVideo 1.5 live in different VRAM neighborhoods, and pretending otherwise is the fastest way to end up with a card that disappoints at one job.
Quick answer: For Flux-only image work, the RTX 5080 16GB is enough thanks to FP8. For HunyuanVideo, you need 24GB minimum and 32GB is honestly the comfort floor. The RTX 4090 is the safe single-GPU pick that covers both workloads without major compromise.
See the recommended pick on the original guide
Who this is for
This guide is for people choosing one GPU to cover both AI image generation (Flux.1 or the new Flux.2 32B) and local AI video (HunyuanVideo 1.5). It's also for buyers prioritizing one workload now but keeping the door open to the other later. If you already know you only care about images, my Flux GPU buyer's guide goes deeper on the image side without the video tax.
VRAM side-by-side: Flux vs HunyuanVideo
The raw numbers tell the story before any opinion enters:
| Workload | Minimum VRAM | Comfortable VRAM | Notes |
|---|---|---|---|
| Flux.1 Dev FP16, 1024px | ~16GB | 24GB | ControlNet pushes this up fast |
| Flux.1 Dev FP8, 1024px | ~12GB | 16GB | Quality loss is minor |
| Flux.2 32B FP16, 1024px | ~28GB | 32GB+ | Locks out everything below RTX 5090 |
| Flux.2 32B FP8, 1024px | ~16GB | 20GB | NVIDIA's May 2026 FP8 path made this viable |
| HunyuanVideo 1.5, 480p | ~14GB | 18GB | With offload, painfully slow |
| HunyuanVideo 1.5, 720p 5s clip | 24GB | 32GB | This is where most users actually want to be |
| HunyuanVideo 1.5, 1080p | 32GB | 40GB+ | Experimental, often unstable on consumer cards |
Two things jump out. First, FP8 saved Flux.2 from being a flagship-only model — a 16GB card now fits it. Second, HunyuanVideo has no equivalent rescue. Tencent's video model is genuinely large, and even aggressive quantization only gets you to ~14GB minimum at low resolution, which isn't what you actually want to generate.
The ceiling matters more than the floor here. Flux tops out around 32GB even in heavy ControlNet stacks. HunyuanVideo wants more the moment you push past 720p, and it doesn't gracefully degrade — you either fit the workload or you wait three times longer with CPU offload.
Speed side-by-side on identical hardware
Same RTX 4090, same room, same patience required:
| Workload | RTX 4090 (24GB) | RTX 5090 (32GB) | RTX 5080 (16GB) |
|---|---|---|---|
| Flux.1 Dev, 1024px | ~14 s | ~7 s | ~16 s |
| Flux.2 32B FP8, 1024px | ~9 s | ~6 s | ~11 s |
| HunyuanVideo, 5s 480p | ~9 min | ~4 min | Not recommended |
| HunyuanVideo, 5s 720p | ~22 min | ~9 min | OOM in practice |
This is the chart that should change your mind if you were leaning toward "I'll just buy the cheaper card." Flux generation times scale linearly and predictably with GPU class. HunyuanVideo times scale brutally — every minute on the 4090 is roughly two minutes on a 3090, and the 5080's 16GB doesn't even fit the video workload at usable quality.
If you're going to use video at all, the GPU you buy needs to land in the green zone on the bottom two rows. There's no in-between.
See the recommended pick on the original guide
Which workload should drive your GPU buy?
The math here is straightforward once you're honest about what you'll actually use.
Image-heavy (Flux is your daily driver, video is a "maybe later"): Buy for Flux. An RTX 5080 16GB at ~$1,000 runs Flux.2 in FP8 around 11 seconds per image and handles Flux.1 LoRAs comfortably. Don't pay the video tax. If video ever becomes a serious workload, rent cloud GPUs for a few months and reassess. For deeper picks on the image side specifically, see the Flux.2 hardware guide.
Video-heavy (HunyuanVideo is the real reason you're upgrading): Don't pretend 16GB is enough. The floor is 24GB and the comfort target is 32GB. The RTX 4090 24GB is the value pick at ~$1,600; the RTX 5090 32GB at ~$2,000 is the right buy if you generate video weekly. My HunyuanVideo GPU breakdown digs into the quantization tradeoffs.
Mixed workload (genuinely both, not "I'll get to video someday"): RTX 4090 24GB is the answer. It's the cheapest GPU that can credibly run both Flux.2 FP8 with ControlNet stacking and HunyuanVideo at 720p with quantization. The RTX 5090 is faster at both but costs 25% more for diminishing returns on the image side.
AI research where you might fine-tune both models: Step up to 32GB. LoRA training for Flux.2 wants ~22GB in FP8 and HunyuanVideo fine-tuning isn't comfortable below 32GB. My AI research GPU guide covers the multi-GPU and bandwidth math for research-grade workloads where you're hopping between model architectures.
Training LoRAs for either model: The training stack matters more than the inference floor. Kohya_ss is the standard tool for Flux LoRA training, and the Kohya_ss training GPU guide walks through batch sizes and memory tricks that change the VRAM picture entirely.
Skip this if you're an occasional video user
Here's the contrarian take most "best GPU" articles won't give you: if you'll generate fewer than ten HunyuanVideo clips a month, don't buy hardware for it. A $2,000 RTX 5090 is roughly 800 hours of RunPod A100 time, and you'll spend maybe 50 hours actually generating video in a year of casual use. Buy an RTX 5080 for your Flux work, rent an A100 when you want to play with HunyuanVideo, and you'll come out ahead on both money and frustration.
The local-vs-cloud break-even for HunyuanVideo specifically lands around 12-15 clips per week. Below that, cloud wins. Above it, the math flips and a 4090 or 5090 pays back inside 18 months.
Common mistakes when picking between these workloads
- Buying a 16GB card hoping to "do video later." This is the most common regret I see. A 5070 Ti or 5080 will run Flux.2 wonderfully and choke on HunyuanVideo. There's no "lighter" video model that solves this — Wan 2.2 and similar alternatives still want 16-24GB to feel usable.
- Assuming HunyuanVideo will get the FP8 treatment Flux.2 got. NVIDIA's FP8 work on Flux.2 was model-specific. Video models have different attention patterns and quantization has historically hurt video coherence more than image quality. Don't bet on a future rescue.
- Sizing for FP16 Flux.2 when FP8 is what you'll actually run. This pushes people toward an unnecessary RTX 5090 when a 4090 or even 5080 would have been fine. FP8 is the default path now, not a workaround.
- Ignoring the storage and time cost of video. A 5-second 720p clip is 30-50MB. Generating 100 clips fills a drive and takes 30+ hours of GPU time. Plan accordingly — fast SSD and a UPS matter more than people admit.
Final verdict
| Your priority | GPU | Why |
|---|---|---|
| Image only (Flux.2 daily) | RTX 5080 16GB (~$1,000) | FP8 fits, ~11s per image, no video tax |
| Image + occasional video | RTX 4090 24GB (~$1,600) | Runs both, the safe single-card answer |
| Video-first (HunyuanVideo weekly+) | RTX 5090 32GB (~$2,000) | Only consumer card that runs 720p video comfortably |
| Budget Flux + cloud video | RTX 4070 Ti Super 16GB (~$700) + RunPod | Best total cost for casual mixed use |
| Research / LoRA training both | RTX 5090 32GB | 32GB is the practical floor for training either model |
See the recommended pick on the original guide
If you're forced to pick one card for both workloads in 2026, the RTX 4090 is the only honest answer.
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Read the full guide on Best GPU for AI — includes our VRAM calculator, GPU comparison table, and live pricing.
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