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: 24GB, and the reasoning is simpler than it used to be. HunyuanVideo has always needed 24GB minimum with 32GB as the comfort floor — and Flux.2 turns out to need 24GB too, because its transformer is ~64GB of BF16 weights and even the 4-bit build is 16GB before the text encoder loads. The RTX 4090 is the safe single-GPU pick for both.
NVIDIA GeForce RTX 4090
24GB GDDR6X24GB runs the 4-bit Flux.2 build with the text encoder offloaded, and HunyuanVideo 1.5 at 720p with quantization. The honest one-card answer for image + video.
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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 BF16, 1024px | 23.8GB | 32GB | The released flux1-dev.safetensors |
| Flux.1 Dev FP8, 1024px | ~12GB | 16GB | Quality loss is minor |
| Flux.2 32B BF16, 1024px | ~64GB | Datacenter | Seven BF16 shards |
| Flux.2 32B FP8, 1024px | ~32GB | Nothing consumer | Half of 64GB, before the encoder |
| Flux.2 32B 4-bit, 1024px | ~16GB | 24GB | The only path that reaches consumer cards |
| 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, the widely repeated claim that NVIDIA’s May 2026 FP8 work made Flux.2 a 16GB model does not survive the file listing — half of 64GB is 32GB, past every consumer card once the text encoder is counted. What actually reaches a consumer GPU is the 4-bit build, which Black Forest Labs’ own example runs on an RTX 4090 with a remote text encoder. Second, HunyuanVideo needs no comparison to look demanding: Tencent’s video model only reaches ~14GB under aggressive quantization, at a resolution nobody wants to generate at.
So the two workloads converge rather than diverge, and both land on 24GB. The ceiling still differs. 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 4-bit, 1024px | ~9 s | ~6 s | ~11 s, nothing spare |
| HunyuanVideo, 5s 480p | ~9 min | ~4 min | Not recommended |
| HunyuanVideo, 5s 720p | ~22 min | ~9 min | OOM in practice |
Times are modelled from memory bandwidth (methodology) rather than measured — the ordering between cards is reliable, the absolute seconds indicative. 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 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.
Check NVIDIA GeForce RTX 5090 on Amazon→Buy on Shopee SG→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 — but that is no longer a 16GB argument. An RTX 5080 16GB at ~$1,400 holds Flux.2’s 4-bit build with nothing left over; a used RTX 3090 at ~$820 holds the same build with room for a ControlNet, for less money. 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 ~$2,200; the RTX 5090 32GB at ~$4,900 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 new GPU that can credibly run both the 4-bit Flux.2 build with a ControlNet resident and HunyuanVideo at 720p with quantization — and a used RTX 3090 does the same for ~$820 if you can accept roughly half the speed. The RTX 5090 is faster at both but costs more than twice as much.
AI research where you might fine-tune both models: Step up to 32GB. Flux.2 LoRA training runs on the 4-bit base rather than FP8, and past rank 32 it wants a 5090’s 32GB, while HunyuanVideo fine-tuning isn’t comfortable below 32GB either. 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 $4,900 RTX 5090 is roughly 2,400 hours of RunPod A100 time at ~$2/hr, 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.
Rent A100 for HunyuanVideo on RunPod→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.” It is the most common regret in this category. A 5070 Ti or 5080 runs Flux.2’s 4-bit build with nothing spare and chokes 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 an FP8 release rescues a model’s VRAM floor. It did not do so for Flux.2 — half of 64GB is still 32GB — and video quantization is harder still. 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 a precision you cannot actually load. Neither BF16 nor FP8 Flux.2 fits a consumer card. Size for the 4-bit build with the encoder offloaded, which is what you will actually run — then buy 24GB so it has room to work in.
- 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 3090 24GB used (~$820) | 4-bit build with ControlNet room, no video tax |
| Image + occasional video | RTX 4090 24GB (~$2,200) | Runs both, the safe single-card answer |
| Video-first (HunyuanVideo weekly+) | RTX 5090 32GB (~$4,900) | Only consumer card that runs 720p video comfortably |
| Budget Flux + cloud video | RTX 4070 Ti Super 16GB (~$800) + RunPod | Best total cost for casual mixed use |
| Research / LoRA training both | RTX 5090 32GB | 32GB is the practical floor for training either model |
NVIDIA GeForce RTX 4090
24GB GDDR6XIf you genuinely need both Flux.2 image generation AND HunyuanVideo 1.5 on the same machine, the 4090's 24GB is the cheapest GPU that runs both without major compromises.
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If you’re forced to pick one card for both workloads in 2026, the RTX 4090 is the only honest answer.
Frequently asked questions
Can I run both Flux.2 and HunyuanVideo on the same GPU?
Yes, if the GPU has at least 24GB of VRAM. The RTX 4090 is the cheapest new card that runs both comfortably — the 4-bit Flux.2 build with the text encoder offloaded, and HunyuanVideo at 720p with quantization. Cards below 24GB will run Flux.2 fine but will struggle or fail outright on HunyuanVideo at usable quality.
Is a 16GB GPU enough for HunyuanVideo?
Not in practice. While HunyuanVideo can technically run with offloading on 16GB cards, generation times become impractical and quality drops noticeably. The realistic floor is 24GB for 720p generation, and 32GB is where the experience becomes comfortable. If video is a real priority, plan around 24GB minimum.
Why doesn’t a 16GB card cover either model properly?
Because the 16GB image tier was never real for Flux.2. Its transformer is roughly 64GB of BF16 weights, so even the 4-bit build is about 16GB before the text encoder, leaving a 16GB card nothing to work in. HunyuanVideo hasn’t received equivalent quantization treatment, and video models historically tolerate aggressive quantization less gracefully than image models because temporal coherence breaks down faster than spatial detail.
Should I buy an RTX 5090 just for HunyuanVideo?
Only if you’ll generate video frequently — roughly 12-15 clips per week is the rough break-even versus cloud rental. For occasional use, renting an A100 or H100 on RunPod or Vast.ai is usually cheaper than buying a $4,900 GPU. The 5090 makes sense for weekly-or-more video work or if you’re combining it with serious Flux.2 training.
Will HunyuanVideo get FP8 optimization like Flux.2 did?
Possibly, but no announcement as of mid-2026. Even if it does arrive, video model quantization has historically been harder than image quantization because temporal coherence is sensitive to precision loss. Don’t buy a smaller GPU today on the assumption that FP8 will rescue video tomorrow.