Best GPU for AI Video Generation: What Actually Runs Wan, LTX-2 & HunyuanVideo

The best GPU for local AI video generation is a 24GB card: a used RTX 3090 (~$800-1,300) for value, an RTX 4090 if you want speed. 24GB is what Wan 2.2's official code asks for — its repo says the TI2V-5B command "can run on a GPU with at least 24GB VRAM (e.g., RTX 4090)" — it clears HunyuanVideo 1.5's stated 14GB minimum with room to spare, and it runs LTX-2.3 without drama. Buying new on a budget? 16GB is the honest floor: NVIDIA's own LTX-2 guide lists 16GB of VRAM (RTX 5070 Ti or higher) as the requirement. And if all you have is a 6GB laptop GPU, FramePack will still make you a 60-second video. Slowly.

Video is a different buying problem than image generation. The models are bigger, the VRAM spikes are nastier, and the gap between "official requirements" and "what the ComfyUI community actually runs it on" is wider than anywhere else in local AI. This guide maps both — the numbers each project publishes, and the quantized reality — so you buy the cheapest card that runs what you actually want to run.

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Quick Answer: Which GPU Should You Buy?

• Best value for serious video work: used RTX 3090 24GB (~$800-1,300 per the June listings recorded in our image-generation GPU ranking; shortage prices move weekly). Runs Wan 2.2 TI2V-5B on the official code path, HunyuanVideo 1.5 comfortably, LTX-2.3 in FP8.
• Fastest 24GB: RTX 4090 — discontinued since late 2024 and scarce (~$2,300+ used), but still the reference card every video repo benchmarks against.
• Cheapest new card that meets NVIDIA's LTX-2 requirement: RTX 5060 Ti 16GB ($429 MSRP) — 16GB of GDDR7 for less than half the price of any 24GB option. The RTX 5070 Ti 16GB ($749 MSRP) is the card NVIDIA's guide actually names, with double the bandwidth.
• No-compromise: RTX 5090 32GB — the only consumer card that fits everything without offloading, but street listings ran to $3,695+ in our July GPU price report. Hard to defend unless you generate daily.
• Already own a 6-12GB card: you are not locked out. FramePack commits to a 6GB floor, and Wan 2.2's 5B model runs on 8GB via community GGUF quants — details in the budget tiers section and our low-VRAM video guide.

One sentence of disclosure: the Amazon links on this page are affiliate links — they cost you nothing and help fund the site.

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What does each video model actually need?

Every number in this table comes from the model's official repo or NVIDIA's published guide — not from spec-sheet guessing. The "community floor" column is what people demonstrably run via GGUF quantization and offloading in ComfyUI; treat it as best-case, not baseline.

Three things worth internalizing before you spend money:

1. The official numbers assume the official code. Wan's 24GB figure is for their reference inference script with offloading flags; the same 5B model runs on an 8GB card once the community GGUF-quantizes it for ComfyUI. Neither number is wrong — they describe different stacks.
2. LTX-2.3 is a 22B model, and 16GB only works because of FP8. The Lightricks LTX-2 repo ships ltx-2.3-22b-dev and ltx-2.3-22b-distilled checkpoints with FP8 quantization support built in; full-precision 22B belongs on workstation hardware, not consumer cards.
3. HunyuanVideo 1.5 is the efficiency story. Tencent rebuilt it at 8.3B parameters (down from the original's much heavier footprint) precisely so the stated minimum could be 14GB. If you own a 16GB card today, it is the most comfortable of the three big models — our HunyuanVideo setup guide covers it end to end.

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Which GPU should you buy at each VRAM tier?

Prices are a mid-2026 snapshot in an ongoing memory shortage — check current listings before deciding, and treat MSRP as fiction for anything above 16GB.

Card specs above are manufacturer launch figures; the street column reflects retail and marketplace listings recorded for our June-July GPU price report, not prices we paid. If you want to sanity-check what a specific card holds before buying, our VRAM calculator does the arithmetic, and the used GPU buying guide covers how not to get burned on a second-hand 3090.

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32GB: The RTX 5090, If You Can Stomach the Price

The RTX 5090 is the best consumer card for video generation and the worst value in the entire stack right now. 32GB of GDDR7 at 1,792 GB/s means Wan 2.2's official code runs without offload flags, LTX-2.3's FP8 pipeline has headroom for the (N×8)+1 frame counts NVIDIA's guide recommends, and 14B-class models stop being a quantization negotiation.

The problem is what it costs. Our GPU price report logged Founders Edition listings at $3,695 against a $1,999 MSRP in mid-July, with Amazon listings around $4,329 and premium AIB models past $5,000. That is memory-shortage pricing, not a value signal.

The honest read: the 5090 earns its price only if you generate video daily and iteration speed is money — a freelancer rendering client clips, not a hobbyist making three videos a weekend. For everyone else, the wait times on a 24GB card are annoying, not disqualifying. If you are weighing the flagship against the previous one, our RTX 5090 vs 4090 benchmark comparison has the workload-by-workload numbers.

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24GB: The Sweet Spot — Used RTX 3090 or RTX 4090

24GB is the tier where video generation stops fighting you. It is the exact number Wan 2.2's repo names for its official TI2V-5B path, it swallows HunyuanVideo 1.5's 14GB minimum without offloading tricks, and it runs LTX-2.3 FP8 with room for longer clips.

The value math strongly favors the used RTX 3090. At ~$800-1,300 (a June listings snapshot — volatile), it costs roughly a quarter of a street-priced 5090 and runs the same model list.

What you give up is time, and you can size that gap from NVIDIA's own published specifications rather than anyone's benchmark:

Diffusion sampling is compute-bound, not bandwidth-bound — the model is resident and the work is repeated denoising passes — so the FP32 column is the one that governs. On paper the 4090 is roughly twice the card for video work despite near-identical memory bandwidth. Treat that as a specification ratio, not a promised speedup: real gaps move with resolution, attention backend and precision. What it does tell you is that the two cards are not close, and that the 3090's case rests entirely on price.

For batch overnight generation the difference barely matters; for iterating on a prompt it very much does. Our RTX 3090 for local AI breakdown covers the card in depth, including what to check before buying used.

For calibration on real generation times at this tier, the official repos are refreshingly specific:

• Wan 2.2 TI2V-5B: a 5-second 720p/24fps clip in under 9 minutes on a single consumer GPU, per the repo's own figure.
• FramePack on an RTX 4090: 2.5 seconds per frame unoptimized, 1.5 s/frame with teacache, per the repo — so a full minute of 30fps video (1,800 frames) is a 45-75 minute job even on the fast card.

If those numbers surprise you, good — that is the honest shape of local video generation in 2026. It is batch work, not chat. A 24GB card makes the batches tolerable. And since the same 24GB opens up the serious LLM tier too, see everything an RTX 4090 runs — video is only half the reason to own one. Current used RTX 3090 listings on Amazon give you today's actual price.

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16GB: The Floor NVIDIA Itself Set

16GB stopped being a compromise tier the day NVIDIA published its LTX-2 guide: the official requirement reads "16GB of VRAM (NVIDIA GeForce RTX 5070 Ti or higher recommended)" with 64GB of system RAM. That is a vendor telling you, in writing, that its FP8 pipeline for a 22B video model targets a $749 card. It is the most concrete buying threshold in this whole market.

What 16GB actually runs:

• LTX-2.3 through the FP8 pipeline NVIDIA's guide describes — 1280×720 for iteration, 1920×1080 finals, 20-30 steps while drafting and 40+ for final renders, with 5-second (121-frame) clips as the recommended shape. LTX-2.3 is also the only open model of the three that generates synced audio with the video in one pass, which matters more than any spec once you start making actual content.
• HunyuanVideo 1.5 comfortably — 16GB clears the stated 14GB-with-offloading minimum with margin.
• Wan 2.2 14B models via community Q5_K_M GGUF quants at 720p, per the community reports we compiled in our Wan ecosystem breakdown.

Within the tier: the RTX 5070 Ti ($749 MSRP, 896 GB/s) is the card NVIDIA names and the better long-term buy; the RTX 5060 Ti 16GB ($429 MSRP, 448 GB/s) fits the same models at half the bandwidth — generation takes meaningfully longer, but nothing on the list stops working. The 5060 Ti is also our best-value pick for image generation for the same reason; our full RTX 5060 Ti 16GB review covers where the bandwidth ceiling bites. Check current RTX 5070 Ti and RTX 5060 Ti 16GB pricing — 16GB cards have so far dodged the worst of the shortage markup.

One warning for laptop buyers: a mobile "RTX 5070 Ti" is not the desktop card — laptop VRAM and bandwidth run lower at the same badge, so check the actual memory spec, not the model name.

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6-12GB: The Budget Paths That Actually Work

You do not need to buy anything to start. Three documented paths exist below 16GB, in ascending order of ambition:

6GB — FramePack. The FramePack repo states its requirement as "at least 6GB GPU memory" on RTX 30/40/50-series cards, generating 60-second, 30fps video with a 13B model by predicting frames next-frame-section style. The catch is time: laptop GPUs like the 3060 run 4x-8x slower than the desktop 4090's 1.5-2.5 s/frame, per the repo — so budget hours, not minutes, for a long clip. GTX 10/20-series cards are explicitly untested.

8GB — Wan 2.2 TI2V-5B via GGUF. The 5B hybrid model fits an 8GB card once quantized for ComfyUI — this is a community path, not Tencent's or Alibaba's official one, but it is well-trodden. Setup details in our Wan video generation guide.

12GB — LTX-2.3 Q3 GGUF and HunyuanVideo offloaded. The community floor for LTX-2.3 sits around a 12GB card running Q3_K_M with offloading, and HunyuanVideo 1.5 runs below its 14GB figure with aggressive offloading at 480p. A ~$300 RTX 3060 12GB is the cheapest desktop card where video generation feels like a workflow rather than a stunt.

Our low-VRAM video generation guide walks all three paths with the exact ComfyUI node setups.

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How do you get set up once the card arrives?

ComfyUI is the runner for all three model families, and the install has not changed:

git clone https://github.com/comfyanonymous/ComfyUI
cd ComfyUI
pip install -r requirements.txt
python main.py

From there, each family needs its model weights and custom nodes — our ComfyUI complete guide covers the manager and workflow basics if this is your first time.

Wan 2.2, official code path (24GB cards) — verbatim from the Wan2.2 repo:

git clone https://github.com/Wan-Video/Wan2.2.git
cd Wan2.2 && pip install -r requirements.txt
huggingface-cli download Wan-AI/Wan2.2-TI2V-5B --local-dir ./Wan2.2-TI2V-5B
python generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ./Wan2.2-TI2V-5B \
  --offload_model True --convert_model_dtype --t5_cpu --prompt "your prompt here"

FramePack (6GB floor) — verbatim from the FramePack repo:

git clone https://github.com/lllyasviel/FramePack
cd FramePack
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
pip install -r requirements.txt
python demo_gradio.py

LTX-2.3 weights (ltx-2.3-22b-dev, ltx-2.3-22b-distilled) come from Hugging Face via the Lightricks LTX-2 repo; on GeForce cards, use the FP8 checkpoints and NVIDIA's recommended settings — 720p while iterating, 20-30 steps, frame counts on the (N×8)+1 rule, 121 frames per clip.

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What should you expect that nobody tells you?

Local video generation in 2026 is real, good, and slow — buy with that expectation or you will be disappointed.

• Minutes per clip is normal. The official figures above — 9 minutes for 5 seconds of Wan 720p on a consumer GPU, 45-75 minutes for a minute of FramePack video on a 4090 — are the honest baseline. Cloud demos have spoiled people; your local card is not an H100.
• The community-floor numbers are best-case. "Runs on 8GB" means someone ran it on 8GB at a given quant, resolution and clip length. Change any of those and you meet the out-of-memory error. When a repo and the community disagree, the repo's number is the one that always works.
• Prices in this guide are listing snapshots, not measurements. The memory shortage has street prices moving weekly — our GPU price report explains why, and nothing above 16GB is selling at MSRP. Check a live listing before you commit.
• No speed figures on this page are ours. The generation times quoted are the model projects' own published numbers, and the 3090-versus-4090 comparison is a ratio of NVIDIA's published specifications. Nothing here was timed on a card in a room.
• Licenses differ per model. Wan 2.2 is Apache 2.0 per its repo — genuinely permissive. LTX-2 and HunyuanVideo 1.5 each ship their own license files; read them before commercial use rather than assuming they match.
• AMD and Mac are second-class citizens here. FramePack is NVIDIA-only by its own README, and the mainstream video workflows assume CUDA. On a Mac, the older LTX-Video line will run, but Apple's top published M-series bandwidth of 400 GB/s is under half an RTX 4090's 1,008 GB/s before you account for diffusion being compute-bound as well. Preview machine, not production machine.
• System RAM matters more than people expect. NVIDIA's LTX-2 requirement includes 64GB of system RAM, and offloading paths on smaller cards lean on it hard. A 16GB-VRAM build with 16GB of system RAM will underperform its own GPU.

For the full model-by-model quality comparison — which of the three actually makes the best-looking clips — see our local AI video generation comparison.

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Sources

• Wan2.2 official repo — TI2V-5B 24GB requirement, generation-time figure, Apache 2.0 license, inference commands
• Lightricks LTX-2 repo — LTX-2.3 22B checkpoints, FP8 quantization support
• NVIDIA RTX AI video generation guide — 16GB / RTX 5070 Ti / 64GB RAM requirement and LTX-2.3 settings
• HunyuanVideo-1.5 repo — 8.3B parameters, 14GB minimum with offloading
• FramePack repo — 6GB floor, supported GPUs, per-frame speed figures
• Our own GPU price report and image-generation GPU ranking — retail and marketplace listing snapshots, June-July 2026
• NVIDIA published specifications for the RTX 3090, 4090, 5060 Ti, 5070 Ti and 5090 — memory bandwidth and FP32 throughput figures used in the comparisons above

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FAQ