The Best GPUs for Running Open-Source AI Video Models in 2026
2026-05-26 · 7 min read · LoreMotion Team
Real-world benchmarks comparing the RTX 3090, 4090, 5090, A6000, A100, and H100 on LTX-Video 2.3, Wan 2.1, and HunyuanVideo — with VRAM requirements, render times, and dollars-per-clip.
We run a small fleet of GPUs to serve LoreMotion's free AI video generation. Over the past year we've benchmarked every major NVIDIA card from the RTX 3060 up to the H100, plus a few AMD options. This post is the result — real timings, real VRAM measurements, and a real total-cost comparison for the GPUs you should actually consider in 2026.
Everything below was measured on the same workload: a 720p / 5-second clip at the default model settings, generated 10 times per card, median time reported. We used Wan2GP for LTX-Video (profile=4, int8 + bf16) and the official inference code for the other models.
TL;DR — what to buy
- Building a single-GPU rig under $2,500: Used RTX 3090 24 GB. Best price-per-performance for open AI video by a wide margin.
- Building a single-GPU rig under $4,000: RTX 4090 24 GB. 40% faster than 3090, same VRAM.
- Building today and budget is no object: RTX 5090 32 GB. The extra 8 GB matters for HunyuanVideo and future longer-clip models.
- Production cloud workload: A100 80 GB on Lambda or RunPod. Best $/GPU-hour for serious throughput.
- Hobbyist on a tight budget: Used RTX 3060 12 GB. Limited to CogVideoX-5B and quantised LTX. Real, but slow.
Detailed breakdowns below.
How AI video models actually use VRAM
Before the benchmarks, a quick note on why VRAM is the headline number for video generation specifically.
A modern text-to-video model is three things stacked on top of each other:
- A text encoder (T5-XXL, Gemma 12B, or similar) that converts your prompt into embeddings — typically 8–12 GB.
- A diffusion transformer that produces the actual video latent — 12–60 GB depending on the model.
- A VAE decoder that turns the latent into pixels — 2–4 GB.
For a 5-second 720p clip these components together can easily peak at 40–60 GB. That's why "raw" Hugging Face inference for LTX-Video originally needed an H100 — until clever framework work (Wan2GP, mmgp, block-swap) figured out how to swap inactive components to system RAM.
The two tricks that matter most for fitting big models on small GPUs:
- Quantisation (int8 / FP8). Cuts VRAM roughly in half with minimal quality loss for video diffusion. Most modern open models tolerate int8 well; some (Mochi) suffer visible colour shifts.
- Block-swap offloading. Keeps only the active transformer block in VRAM and streams the rest from system RAM. Adds 10–25% to wall-clock time but enables a 22B-parameter model to fit on 24 GB.
With both techniques applied, LTX-Video 2.3 fits on an RTX 3090. Without them, you need an H100. This is the difference between "this model is for research labs" and "this model is for any creator with a gaming PC".
Benchmark: LTX-Video 2.3 (Wan2GP, profile=4, 720p / 5s)
| GPU | VRAM | Wall-clock | Cost-new (US) | $/clip @ 100% util |
|---|---|---|---|---|
| RTX 3060 12 GB | 12 GB | OOM | $300 | — |
| RTX 3090 24 GB | 24 GB | 72s | $750 (used) | $0.0008 |
| RTX 4090 24 GB | 24 GB | 44s | $2,200 | $0.0014 |
| RTX 5090 32 GB | 32 GB | 31s | $2,400 | $0.0010 |
| RTX A6000 48 GB | 48 GB | 58s | $4,800 | $0.0033 |
| A100 80 GB SXM | 80 GB | 28s | cloud only | $0.014/hr → $0.0001 |
| H100 80 GB SXM | 80 GB | 19s | cloud only | $0.025/hr → $0.00013 |
Notes on this table:
- "OOM" for the 3060 means LTX-Video 2.3 will not load on 12 GB even with int8 + block-swap. CogVideoX-5B does run on the 3060.
- The A6000 is slower than the 4090 despite having twice the VRAM. This surprises people. It's because the A6000 is built on the older Ampere architecture (same as 3090) and has lower memory bandwidth than Ada or Blackwell. VRAM matters for fitting models, not for running them.
- The "$/clip" column for consumer cards assumes 24/7 uptime over a 3-year amortisation. Real-world utilisation is lower, but the ratio between cards is what matters.
- Cloud GPU rates are typical RunPod/Lambda spot pricing in May 2026.
Benchmark: HunyuanVideo (720p / 5s, full precision unless noted)
| GPU | VRAM | Wall-clock | Notes |
|---|---|---|---|
| RTX 3090 24 GB | 24 GB | OOM | Won't fit, even at int8 |
| RTX 3090 + GGUF Q4 | 24 GB | 240s | Runs but visible quality loss |
| RTX 4090 + GGUF Q5 | 24 GB | 145s | Best quantised consumer option |
| RTX 5090 + GGUF Q8 | 32 GB | 120s | Near-full quality, fits cleanly |
| RTX A6000 48 GB | 48 GB | 95s | Full precision, comfortable |
| A100 80 GB | 80 GB | 78s | Reference benchmark |
| H100 80 GB | 80 GB | 52s | Fastest by a wide margin |
HunyuanVideo is the model where the 80 GB cards earn their price. Below 48 GB you're forced into quantisation, and HunyuanVideo's quality drop with int8 / int4 is more visible than LTX's. If HunyuanVideo is what you want to run, budget for an A6000 minimum.
Benchmark: Wan 2.1 14B (720p / 5s)
| GPU | VRAM | Wall-clock |
|---|---|---|
| RTX 3090 24 GB | 24 GB | 110s |
| RTX 4090 24 GB | 24 GB | 68s |
| RTX 5090 32 GB | 32 GB | 49s |
| A100 80 GB | 80 GB | 42s |
Wan 2.1 fits cleanly on any 24 GB card without quantisation tricks. It's the easiest open model to set up on commodity hardware.
Why we run RTX 3090s in production
For LoreMotion specifically — free service, ad-supported, LTX-Video 2.3 as the default model — the RTX 3090 is the unambiguous winner on dollars-per-clip. A used 3090 in 2026 sells for $700–$900. At 72 seconds per clip that's roughly 1,200 clips per day per card if we kept it pegged at 100% utilisation (we don't, but the upper bound matters).
The RTX 4090 is faster (44s/clip vs 72s) but costs three times as much. The marginal cost per clip works out worse than the 3090. The RTX 5090 has the same problem but with even better speed — it's the right choice if you're capacity-constrained on a single rig, not the right choice if you can just add another 3090.
Cloud GPUs (A100, H100) win on raw speed but lose on cost once your utilisation is consistently above ~30%. For a service like ours that has stable demand, owned hardware is cheaper. For a hobbyist running one render per week, RunPod's hourly A100 at ~$1.40/hr is the better choice.
What about AMD?
We tested the RX 7900 XTX 24 GB and the Radeon Pro W7900 48 GB. Both technically run LTX-Video via ROCm + PyTorch's ROCm build, but:
- Wan2GP's int8 quantisation path doesn't work on ROCm — you're forced into full precision, which OOMs on 24 GB.
- The ROCm PyTorch builds are 6–18 months behind CUDA. Every new model launch has a 3-month period where the only working backend is CUDA.
- Even when everything works, the W7900 was 35% slower than an A6000 of comparable price.
Unless you have a strong reason to run AMD (data sovereignty, supply chain, existing infrastructure), NVIDIA is the practical choice for AI video in 2026.
What about Apple Silicon?
M2 Ultra and M3 Max can technically run smaller models (CogVideoX-5B, quantised Mochi). MLX support for the diffusion transformer architectures used by LTX, Wan, and HunyuanVideo is patchy. We tested an M3 Max 128 GB and a Mac Studio with M2 Ultra — both ran CogVideoX-5B usably (180–240s per clip) but couldn't load LTX or HunyuanVideo cleanly.
If you already own an Apple Silicon machine, run CogVideoX-5B and see if the output quality meets your needs. If you're buying hardware specifically to generate AI video, buy NVIDIA.
VRAM-first decision guide
If you're optimising for one number, that number is VRAM:
- 12 GB: CogVideoX-5B only. Useful for learning and prototyping.
- 16 GB: Wan 2.1 1.3B variant, quantised CogVideoX. Still mostly research-grade output.
- 24 GB: The sweet spot. LTX-Video 2.3 via Wan2GP, Wan 2.1 14B, quantised HunyuanVideo. Used 3090s or new 4090s.
- 32 GB: RTX 5090 territory. Adds breathing room for full-precision HunyuanVideo at Q8 and future longer-clip models.
- 48 GB: A6000 territory. Full-precision HunyuanVideo, fine-tuning on smaller models.
- 80 GB: A100 / H100 cloud. Fine-tuning, multi-clip batched inference, production throughput.
What we'd buy today
Spending our own money in May 2026, the picks are:
- First card (any budget): Used RTX 3090. The price-performance gap to anything else is too big to justify.
- Second card for the same rig: Another used 3090. Two 3090s = roughly one 5090 for less money.
- If you must buy new: RTX 5090. The extra 8 GB matters more than the speed bump.
- For cloud bursts: RunPod or Lambda H100 spot instances. Sub-$3/hr when you can find them.
If you want to test these models without buying hardware at all, LoreMotion runs LTX-Video 2.3 on 3090s and gives you the first clip free with no signup. We'll add Wan 2.1 and possibly HunyuanVideo later in 2026 once we can do it at a sustainable cost per clip.