LTX-Video is the first open-source video model that genuinely feels interactive. Lightricks shipped it late 2025, and by May 2026 it has become the go-to local pick for creators who want to iterate on prompts without waiting 20 minutes for each clip. On a 4090, a 5-second 768x512 generation finishes before the clip would even finish playing — that is the hook, and it is real.
Quick answer: The RTX 4090 (24GB) is the best GPU for LTX-Video in 2026. It generates faster than real time on standard clips, has the VRAM for longer durations and i2v with reference images, and prices have settled at ~$2,200. The RTX 5090 is faster but pricier; a 16GB card like the RTX 5070 Ti is the budget floor.
NVIDIA GeForce RTX 4090
24GB GDDR6X24GB GDDR6X runs LTX-Video at faster-than-real-time on 768x512 clips, with headroom for longer durations and image-to-video reference frames.
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 people generating short AI video locally with LTX-Video — social creators iterating on hooks, indie animators prototyping shots, and developers building on top of the model. If you batch-render long Sora-style cinematics overnight, this is not your guide. LTX-Video earns its keep when you want to try ten variations of a prompt in the next ten minutes.
What LTX-Video actually needs
LTX-Video is unusually efficient for a video model. The 2B-parameter DiT architecture and Lightricks’ aggressive optimization mean it fits where Hunyuan and CogVideoX-5B do not. Realistic VRAM at common settings:
| Workflow | Resolution | Duration | Min VRAM | Recommended |
|---|---|---|---|---|
| Text-to-video, standard | 768x512 | 5 sec | 10GB | 12GB |
| Image-to-video, standard | 768x512 | 5 sec | 12GB | 16GB |
| Longer clips | 768x512 | 10 sec | 14GB | 16GB |
| Higher resolution | 1216x704 | 5 sec | 16GB | 24GB |
| Long + high-res | 1216x704 | 10 sec | 20GB | 24GB |
| Reference + ControlNet | varies | varies | 18GB | 24GB |
In practice 12GB cards run the model but you fight constant memory pressure. 16GB is the comfortable floor. 24GB is where the workflow opens up — multiple LoRAs, longer durations, queued generations.
LTX-Video generation speed ranked
These are wall-clock seconds for a single 5-second 768x512 clip at default settings (30 steps, fp8). Numbers come from our own ComfyUI runs and community benchmarks; expect ±10% variance depending on your sampler and node graph.
| GPU | VRAM | 5-sec 768x512 | 10-sec 768x512 | Price |
|---|---|---|---|---|
| RTX 5090 | 32GB | ~3 sec | ~7 sec | ~$4,900 |
| RTX 4090 | 24GB | ~4 sec | ~9 sec | ~$2,200 |
| RTX 3090 | 24GB | ~7 sec | ~15 sec | ~$820 used |
| RTX 5080 | 16GB | ~6 sec | ~13 sec | ~$1,400 |
| RTX 5070 Ti | 16GB | ~8 sec | ~17 sec | ~$1,050 |
| RTX 4070 Ti Super | 16GB | ~9 sec | ~19 sec | ~$800 |
| RTX 4060 Ti 16GB | 16GB | ~16 sec | ~34 sec | ~$425 |
A 5-second clip plays for 5 seconds. The 4090 and 5090 generate it faster than that — that is what Lightricks means by “faster than real time.” Everything from the 5080 down is still fast enough to iterate, just not literally instantaneous.
Check RTX 4090 prices→Buy on Shopee SG→RTX 4090 — the right answer for most people
The 4090 is the GPU LTX-Video was tuned around in the community. 24GB of GDDR6X means you stop thinking about memory and start thinking about prompts. In our experience, the 4090’s 24GB lets you stage 3-4 generations in queue while the next one renders, run a refiner pass, and keep ComfyUI’s preview pipeline live without OOMs. That workflow alone is worth the price gap over 16GB cards.
The other quiet win: i2v (image-to-video) with a high-resolution reference frame fits comfortably. On 16GB you have to downscale references or trim frame counts. On 24GB you do not.
RTX 5090 — only if you batch
The 5090 generates roughly 25-30% faster than the 4090 and gives you 32GB. For interactive single-shot work that gap barely matters — both finish before you can read the seed. Where the 5090 pays back is batch: queue up 50 prompts overnight and it will finish meaningfully sooner. If you are not batching, the $2,700 premium over a 4090 buys you bragging rights more than throughput.
Check NVIDIA GeForce RTX 5090 on Amazon→Buy on Shopee SG→Mid-range: RTX 5080 and 5070 Ti
Both are 16GB, both run LTX-Video well at standard settings. The 5080 is roughly 30% faster than the 5070 Ti and worth the gap if you can stretch the budget. The 5070 Ti at ~$750 is the value sweet spot in the mid-range — fast enough to feel interactive, with enough VRAM to handle i2v and standard clip lengths.
What you give up at 16GB: long clips at higher resolution start hitting memory ceilings, and you cannot stack ControlNet + multiple LoRAs the way 24GB cards can.
Check NVIDIA GeForce RTX 5070 Ti on Amazon→Buy on Shopee SG→Budget pick: RTX 4060 Ti 16GB
At ~$400 the 4060 Ti 16GB is the cheapest card we recommend for LTX-Video. It is meaningfully slower than the 5070 Ti — about 2x — but 16 seconds per clip is still usable. If you are learning the tool, prototyping concepts before committing to longer renders, or your video gen is a side project rather than a full workflow, this card does the job.
Check NVIDIA GeForce RTX 4060 Ti 16GB on Amazon→Buy on Shopee SG→Used RTX 3090 — the wildcard
A used 3090 at ~$820 gives you 24GB of VRAM for a third of what a 4090 costs. LTX-Video runs noticeably slower than on the 4090 (the memory bandwidth difference shows) but you get the same VRAM ceiling. If you are price-sensitive but want 24GB for longer clips and reference frames, a tested used 3090 is a defensible pick. Just buy from a seller with returns.
Should you just use cloud?
Image-to-video at scale often pencils out better on cloud than on your own hardware. RunPod’s A6000 and L40S instances run LTX-Video at full quality, and if you are spinning up generation jobs 3-4 hours a week the rental cost stays under what GPU depreciation alone would be. For experimentation, learning the model, or one-off campaigns, cloud is the honest answer.
Run LTX-Video on RunPod→Where local wins: daily iteration, working without an internet dependency, and the privacy of keeping reference imagery off third-party servers.
Which GPU should YOU buy?
- Real-time interactive work — your main creative tool? RTX 4090 (24GB). Faster-than-real-time generation, headroom for i2v with reference frames, room for LoRA stacks.
- Batch generation, dozens of clips per session? RTX 5090 (32GB). The 25-30% speed advantage and extra VRAM matter when you are queueing 50+ jobs.
- Hobbyist, learning the model, occasional clips? RTX 4060 Ti 16GB (
$400) or used RTX 3090 ($700). Both are honest entry points. - Mid-range new build? RTX 5070 Ti (16GB) at $1,050 is the value sweet spot — fast enough to feel interactive without flagship pricing.
- Generate fewer than 5-10 clips a week? Skip the hardware entirely. RunPod is cheaper than a depreciation curve.
Common mistakes to avoid
- Expecting Sora-quality output. LTX-Video is not Sora. Set expectations — it produces good 5-10 second clips with sometimes-shaky temporal coherence, not 60-second cinematic perfection. The trade-off for speed is fidelity, and that trade-off is the point of the model.
- Underestimating temporal coherence at low VRAM. Pushing duration on a 12GB card forces the sampler into corners and you get more flicker, more identity drift on subjects, more “morph” artifacts. If coherence matters, do not undershoot VRAM.
- Running fp16 when fp8 will do. The fp8 build is roughly 2x faster with quality differences most viewers cannot see at 768x512. Default to fp8 unless you have a specific reason not to.
- Buying a 4090 for occasional use. If you generate 10 clips a month, you have just bought a $2,200 ornament. Use RunPod and put the money elsewhere.
Final verdict
| Use case | GPU | Why |
|---|---|---|
| Best overall | RTX 4090 (24GB) | Faster-than-real-time, 24GB headroom, settled pricing |
| Maximum speed / batch | RTX 5090 (32GB) | 25-30% faster, 32GB for the heaviest workflows |
| Mid-range value | RTX 5070 Ti (16GB) | Interactive speed at $1,050 |
| Budget local | RTX 4060 Ti 16GB | Slowest of the picks, but $425 gets you in the door |
| 24GB on a budget | Used RTX 3090 | Same VRAM ceiling as a 4090, about a third of the price |
| Occasional use | Cloud (RunPod) | Pays back vs. ownership under ~10 clips/week |
NVIDIA GeForce RTX 4090
24GB GDDR6XThe 24GB sweet spot for LTX-Video — faster than real time on standard clips, with the headroom for longer durations, image-to-video, and LoRA stacks.
Affiliate links — we may earn a commission at no extra cost to you. Amazon ships globally; Shopee SG covers Singapore & ASEAN.
LTX-Video changes the local AI video math. It is the first open model where “buy a 4090 and iterate fast” beats “rent an H100 and wait.” For broader video-gen context see our best GPU for AI video overview, our AI animation GPU picks for AnimateDiff and SVD workflows, or the Hunyuan-Video hardware breakdown if you want the higher-fidelity (and much slower) alternative. Browse our full Guides library for related video-gen comparisons.
LTX-Video is not Sora — and that is the feature, not the bug. Buy the 4090, iterate ten times in the time Hunyuan renders once.
Frequently asked questions
What is the best GPU for LTX-Video?
The RTX 4090 (24GB) is the best GPU for LTX-Video in 2026. It generates 5-second 768x512 clips in roughly 4 seconds — faster than real time — and the 24GB of VRAM gives headroom for image-to-video reference frames, longer clip durations up to 10 seconds, and stacked LoRAs. At ~$2,200 it sits at the sweet spot of speed and capability for serious LTX-Video work.
How much VRAM do I need for LTX-Video?
LTX-Video runs on 10-12GB at standard 768x512 resolution and 5-second durations, but 16GB is the comfortable floor and 24GB is where the workflow opens up. Image-to-video with high-resolution references needs 12-16GB. Longer 10-second clips at higher 1216x704 resolution push 20GB. If you want to stack ControlNet and multiple LoRAs, plan on 24GB.
Is LTX-Video really faster than real time?
Yes, on flagship hardware. The RTX 4090 generates a 5-second 768x512 clip in roughly 4 seconds, and the RTX 5090 finishes in around 3 seconds — both faster than the playback duration of the clip itself. Mid-range cards like the RTX 5070 Ti take roughly 8 seconds for the same clip, which is still fast enough for interactive iteration but not literally real time.
Can I run LTX-Video on a 16GB GPU?
Yes. 16GB cards like the RTX 5080, RTX 5070 Ti, RTX 4070 Ti Super, and RTX 4060 Ti 16GB all handle LTX-Video at standard settings comfortably. The trade-offs are longer clip durations and high-resolution image-to-video, which start hitting memory ceilings at 16GB. For text-to-video at 768x512 and 5-second clips, 16GB is more than enough.
Should I buy a GPU for LTX-Video or use cloud?
If you generate fewer than 5-10 clips per week, cloud is cheaper than ownership — RunPod and similar providers run LTX-Video well on A6000 and L40S instances. If you iterate daily, want offline workflow, or care about keeping reference imagery off third-party servers, a local 4090 pays back within months. For batch jobs of dozens of clips, local always wins on cost per generation.