Wan 2.1, 2.2, and 2.7 for Local AI Video Generation: Which GPU Can Actually Run It (2026 Guide)

By RunAIHome Team · 17 min read
local-aigpuvideo-generationwancomfyui

TL;DR: The Wan 2.2 14B is today’s best open-source local video model, but at full precision it needs 54+ GB of VRAM — datacenter territory. The fix is a two-step trick (GGUF quantization + T5-XXL CPU offload) that drops GPU VRAM from 54 GB to 6–8 GB for 480p or 12–16 GB for 720p. At 16 GB VRAM, you get 720p clips in 2–4 minutes. Wan 2.7 (April 2026) raises the bar to 4K but still targets 24 GB as its practical minimum.

RTX 4070 12GBRTX 5060 Ti 16GBRTX 4090 24GB
Best forWan 14B at 480p (GGUF)Wan 14B at 720p (FP8)Wan 2.7, no compromises
Street price (Jun 2026)~$430 used$429 new$2,200–2,755
Peak VRAM (GGUF + offload)~8 GB~12–14 GB~22 GB full FP8
480p 5-sec clip (Wan 2.2)~18–22 min~8–12 min~5 min
720p 5-sec clip (Wan 2.2)impractical (>60 min)2–4 min3–5 min
The catchVRAM ceiling blocks 720p128-bit bus limits bandwidth vs. 4070Supply-constrained, $2,200+ entry

Honest take: The RTX 5060 Ti 16GB at $429 is the new sweet spot for Wan 2.2. At 16 GB GDDR7 and 448 GB/s bandwidth, it handles 720p clips in 2–4 minutes — the same tier as the $900+ RTX 4080 Super — for less than half the price. The RTX 4090 is worth it only if you need Wan 2.7 or are running production-scale batches.

What the Wan Series Actually Is

Wan (万象, “ten thousand forms”) is Alibaba’s open-source AI video generation model family, released under Apache 2.0. Unlike most commercial video generators that require cloud API access, the Wan weights are available to download and self-host. There are no per-minute charges once you have the model locally.

Four major versions have shipped since early 2025:

  • Wan 2.1: Dense transformer architecture, text-to-video and image-to-video. The version that put open-source video generation on the map for home lab builders.
  • Wan 2.2: Switched to Mixture of Experts (MoE) — 27B total parameters with 14B active per step. Better quality than 2.1 at similar compute cost, and now capable of 720p on consumer hardware.
  • Wan 2.5 / 2.6: Iterative improvements — camera control, better prompt adherence, consistent character generation.
  • Wan 2.7 (released April 22, 2026): 4K-capable, up to 20-second clips, richer instruction following. Same 14B architecture, heavier output demands.

All versions share the same inference stack. A machine you build for Wan 2.2 today will run Wan 2.7 — you swap the checkpoint, not the hardware.

Three Model Sizes, Three Use Cases

The Wan family ships in three sizes:

1.3B (text-to-video only) — the GPU-poor tier. The T2V-1.3B checkpoint needs 8.19 GB VRAM with no tricks. An RTX 4060 8GB generates a 5-second 480p clip in around 4–6 minutes. Quality is noticeably lower than the 14B model, but it’s usable for rapid prompt iteration and creative experimentation on budget hardware.

5B (Wan 2.2 and later) — the mid-tier. Introduced with Wan 2.2’s MoE architecture. Runs cleanly at 480p on any 12 GB card without heavy optimization, and can generate 720p @ 24 fps on a single RTX 4090. A better choice than the 14B if your card has exactly 12 GB VRAM.

14B (text-to-video + image-to-video) — the quality tier. This is where Wan competes with commercial video APIs. The 14B produces the cinematic motion, coherent character movement, and high fidelity that made the model famous. It’s also where the VRAM math gets painful.

The VRAM Ceiling Problem — and the Fix

The Wan 2.2 14B pipeline has two major memory consumers:

  1. The video diffusion transformer itself: ~14 GB in FP8, ~28 GB in FP16
  2. The T5-XXL text encoder: ~9.4 GB at FP16

At full precision, the combined pipeline needs 54–65 GB VRAM. No consumer GPU has that. Even the RTX 5090’s 32 GB falls short.

The community has converged on a two-step fix that makes Wan 14B viable on surprisingly modest hardware:

Step 1 — Quantize the transformer. GGUF Q4 or Q5 weights reduce the main Wan 14B model from ~28 GB to approximately 8–8.5 GB. Quality loss versus FP16 is minimal at 480p — most viewers can’t identify the difference in blind tests. At 720p there’s a subtle softening in fine detail, but the practical output remains strong.

Step 2 — Offload T5-XXL to CPU RAM. T5-XXL is only used during the conditioning pass at the start of each generation. If you have 32+ GB of system RAM, T5 can live in CPU RAM and be called when needed. This costs you 20–30 seconds of extra conditioning time per clip but saves 9+ GB of GPU VRAM. With both tricks applied:

  • GPU VRAM at 480p: ~6–8 GB
  • GPU VRAM at 720p: ~12–16 GB

This is how the RTX 4070 12GB runs the Wan 14B at all — not natively, but via GGUF + T5 offload.

One requirement that trips up first-timers: you need at least 32 GB of system RAM. With T5-XXL parked in CPU RAM and your diffusion model in VRAM, 16 GB of system RAM will hit swap during the conditioning pass and cause either errors or extremely slow generation. 32 GB is the minimum; 64 GB is comfortable.

Benchmark Data: Real Generation Times

The table below comes from SaladCloud’s published Wan 2.1 T2V-14B benchmarks, testing a 5-second clip at 480p and 720p with no quantization or offloading — full precision, official inference script.

GPUVRAM480p (5-sec clip)720p (5-sec clip)
H100 SXM80 GB85 sec284 sec
A100 SXM80 GB170 sec523 sec
A4048 GB501 sec1,083 sec
RTX 409024 GB281 secOOM
RTX 309024 GB—OOM

Two things stand out:

First, the RTX 4090 at 281 seconds beats the enterprise A40 at 501 seconds despite the A40 having twice the VRAM. GDDR6X bandwidth (1,018 GB/s on the 4090 vs. PCIe A40) matters more than raw CUDA core count for diffusion inference — the model is memory-bandwidth-bound, not compute-bound.

Second, both the RTX 4090 and RTX 3090 OOM at 720p with Wan 2.1 full precision. Running Wan 14B at 720p full-precision requires more VRAM than any consumer GPU has.

Wan 2.2 changes the 720p picture. The switch to MoE architecture (27B total, 14B active) enables efficient high-resolution generation with quantization. With FP8 + T5 offload, the RTX 4090 can now generate 720p clips. At 16 GB, the RTX 4080 Super generates 720p clips in 2–4 minutes with the same setup.

For the RTX 3090 specifically: a community benchmark running Wan 2.2-Animate on a 3090 recorded approximately 7 seconds per frame at 640×480 — meaning a 5-second, 81-frame clip takes roughly 9–10 minutes. At 720p that climbs to ~18 seconds per frame, or around 24 minutes per clip. Workable for overnight batches or one-off generates; not for rapid iteration.

GPU Tier Guide

8 GB VRAM — Wan 1.3B or 5B only

The RTX 4060 8GB, RTX 5060 8GB, and RX 7700 XT sit at the 8 GB tier. Wan 1.3B is native; Wan 2.2 5B runs with light quantization at 480p. The 14B is technically possible with aggressive GGUF + CPU offload, but generation times run 20–30 minutes per 5-second clip — barely usable for iteration.

If your GPU is 8 GB, use Wan 2.2 5B rather than fighting the 14B. The 5B at 8 GB produces output that’s meaningfully better than the 1.3B, without the wait.

12 GB VRAM — Wan 14B at 480p (slow but real)

The RTX 4070 12GB and RTX 3060 12GB can run Wan 14B GGUF + T5-CPU offload at 480p. Peak GPU VRAM during generation: ~8 GB, leaving about 4 GB headroom. Generation times are 18–22 minutes per 5-second 480p clip.

The RTX 4070 has 504 GB/s bandwidth (GDDR6X, 192-bit bus). Bandwidth isn’t the limiter here — VRAM is. You have enough bandwidth for Wan 14B; you don’t have enough VRAM to skip the offloading tricks, which is what slows you down.

At 720p on 12 GB: possible with extreme quantization (Q3 or lower), but generation time exceeds 60 minutes per clip. Not practical for creative workflows.

If you’re choosing between the base RTX 4070 (12GB) and the RTX 4070 Super (also 12GB): both have the same 504 GB/s bandwidth and same VRAM ceiling for Wan. Get whichever is cheaper used. Used 4070s in June 2026 are running $400–450.

16 GB VRAM — The Sweet Spot

This is the tier where Wan 2.2 14B becomes genuinely usable for creative work. At 16 GB with FP8 + T5 offload:

  • Peak VRAM during 720p generation: ~12–14 GB
  • Generation time: 2–4 minutes per 5-second clip
  • Same performance tier as the RTX 4080 Super 16GB for this workload

The RTX 5060 Ti 16GB ($429 at MSRP) is the standout value at this tier. It delivers 448 GB/s from GDDR7 across a 128-bit bus. That’s less than the RTX 4070’s 504 GB/s on a wider bus, but you’re gaining 4 GB of VRAM over the 4070 — and for Wan 14B at 720p, those 4 GB are what make the difference between feasible and impractical.

Compared to the RTX 4060 Ti 16GB (the previous 16 GB value card at ~$280–320 used): the 5060 Ti’s 448 GB/s is 56% faster than the 4060 Ti’s 288 GB/s at the same VRAM. For diffusion inference that means noticeably fewer seconds per denoising step. The 5060 Ti generates 720p clips in 2–4 minutes; the 4060 Ti at 16 GB takes 4–7 minutes for the same clip.

The RTX 5070 Ti (16GB, 960 GB/s GDDR7) is even faster but costs ~$750+. For pure Wan 2.2 throughput, the extra bandwidth helps — but the 5060 Ti gets you 80% of the way there at 57% of the cost.

24 GB VRAM — Comfortable and Wan 2.7-Ready

At 24 GB, you can run Wan 2.2 14B FP8 fully on GPU — no T5 CPU offload needed, no conditioning delay. That shaves 20–30 seconds per clip and enables tighter iteration loops. You also get enough headroom for Wan 2.7’s higher-resolution workflows.

The RTX 4090 24GB is the primary 24 GB consumer option today. In June 2026, new units list at $2,755 on Amazon; used units trade at $2,200–2,350 on eBay. RTX 40-series has been discontinued, so supply will only tighten from here.

The RTX 3090 24GB gives the same VRAM at significantly lower cost — used prices are $800–1,050 on eBay in June 2026. Bandwidth is 936 GB/s vs. the 4090’s 1,018 GB/s, a ~9% difference that translates to roughly 10–15% slower generation times for Wan 14B. For 720p Wan 2.2 workflows, a 3090 is a competent machine. For Wan 2.7’s 4K outputs the bandwidth gap starts to matter more, but even there it remains functional.

If you’re budget-constrained and the choice is RTX 5060 Ti 16GB vs. RTX 3090 24GB: the 3090 wins for video generation. The extra 8 GB of VRAM enables no-compromise Wan 2.2 workflows and gives you a path to Wan 2.7 without reaching VRAM limits.

Setting Up Wan Locally: The Software Stack

ComfyUI is the standard frontend. The community ComfyUI-WanVideoWrapper node handles model loading, T5 offloading, GGUF support, and FP8 quantization in a single workflow file.

For lower VRAM setups, Wan2GP is worth installing alongside ComfyUI. It reduces VRAM consumption further via memory-efficient attention and block-wise offloading, enabling 10-second 720p clips on an RTX 4090 and 480p clips on 8 GB cards.

A working Wan 14B workflow on 16 GB looks like this:

Load Wan 14B GGUF Q5 checkpoint  →  ~8.5 GB VRAM
Load T5-XXL to CPU RAM           →  ~9.4 GB system RAM
Load CLIP vision encoder          →  ~1.5 GB VRAM
--
Peak VRAM during denoising (720p): 12–14 GB
System RAM required: 32 GB minimum

The conditioning step (T5 encode pass) runs on CPU and adds 20–30 seconds of wait before denoising starts. For a single generate that’s noticeable; for batch generation of 10+ clips with the same prompt, you pay the cost once.

Common error for 12 GB users: CUDA out of memory during sampling. Fix: first, check that enable_sequential_cpu_offload is active in the ComfyUI node — this enables step-by-step VRAM management. If still OOM, drop resolution from 720p to 480p, or switch from GGUF Q5 to Q4. If using Q4 and still hitting OOM, your system RAM may be bottlenecking the offload (16 GB system RAM is not enough — upgrade to 32 GB before troubleshooting further).

Wan 2.7: What Changed, and Whether It Changes Your Hardware Decision

Wan 2.7 launched April 22, 2026. The weights are on Hugging Face under Apache 2.0.

The architecture is still 14B parameters (MoE, same family as 2.2), so the inference stack is the same. What changed is the output capability: 4K resolution, up to 20-second clips, and a significantly improved instruction-following system that understands natural camera and lighting descriptions rather than requiring technical syntax.

Practical minimum for Wan 2.7: 24 GB VRAM. At 16 GB with GGUF + T5 offload, Wan 2.7 can generate 720p clips (5–10 seconds). Generating 4K at any length pushes the 16 GB card to its limit — generation times run 15–30+ minutes per clip. Not practical for iteration.

The 3090 vs. 4090 question for Wan 2.7: Both have 24 GB. At 720p with Wan 2.7, both work well. At 4K, the 4090’s bandwidth advantage (1,018 vs. 936 GB/s) matters more because 4K frame data is substantially heavier per denoising step. The 3090 can generate 4K but takes 30–50% longer per clip.

If your primary goal is Wan 2.7 at 4K, budget for a 4090. If your primary goal is Wan 2.2 at 720p, a used 3090 at $800–1,050 is the smart buy.

What to Actually Buy in June 2026

Budget under $500 — RTX 5060 Ti 16GB ($429)

The top pick for Wan 2.2 video generation under $500. 720p clips in 2–4 minutes, available at MSRP, 16 GB GDDR7. Nothing at this price tier competes for this workload. The Wan 2.2 14B FP8 + T5-offload workflow runs cleanly, and the card handles everything else (LLM inference up to 20B, ComfyUI image generation) without compromise.

Already have a 12 GB card (RTX 4070, RTX 3060)

Don’t upgrade just for Wan 2.2 at 480p — it works, just slowly. The right upgrade trigger is when you’re routinely waiting 20+ minutes per clip and need tighter iteration. At that point, the jump to 16 GB VRAM (RTX 5060 Ti 16GB) is the minimum viable upgrade.

Need Wan 2.7 or production throughput

RTX 3090 used ($800–1,050) if budget is the constraint; RTX 4090 used ($2,200–2,350) if you want the comfortable experience at 4K. The 5090 at 32 GB is only justified for multi-card configurations or fine-tuning.

No GPU and uncertain about commitment

Run Wan 2.2 on RunPod first. An A40 48 GB pod runs at approximately $0.54/hour — enough to test 10–15 clips and decide whether video generation is actually part of your workflow before committing to hardware. The first time Wan 14B OOMs on your setup at 2 AM is a frustrating way to learn you needed 24 GB.

FAQ

Can the RTX 5070 12GB run Wan 14B? Yes — same 12 GB ceiling as the RTX 4070, same 480p GGUF workflow, similar generation times. The 5070’s 672 GB/s bandwidth is faster than the 4070’s 504 GB/s, so each denoising step is quicker, but you’re still memory-constrained at 720p. Expect 12–15 minutes per 480p clip rather than 18–22 on the 4070. Still not 720p territory.

Does AMD ROCm support Wan? Yes, with caveats. ROCm 6.x + the community Wan2.1-AMD fork works on the RX 7900 XTX (24 GB). Performance roughly matches the RTX 3090 at 480p. Setup is notably more involved than CUDA — expect a few hours of troubleshooting. For AMD users with 24 GB, it’s a viable path but not a plug-and-play experience.

Is Wan better than Sora, Kling, or Runway? For hobbyist 480p–720p generation at zero ongoing cost: comparable to mid-tier commercial APIs for many prompts, worse on complex motion and multi-scene clips. For 4K production work: commercial APIs remain ahead on speed and polish. Wan’s edge is economics — once downloaded, every clip is free, and you’re not rate-limited.

How much storage do I need? Wan 2.2 14B GGUF Q5 checkpoint: ~8.5 GB. T5-XXL text encoder: 9.4 GB. CLIP model: ~1.5 GB. Total per model version: ~20 GB. Keep the models on an NVMe SSD — the Samsung 990 Pro 2TB loads the full checkpoint in under 10 seconds vs. 90+ seconds on a spinning drive. Slow model load times compound badly when iterating on prompts.

Can Apple Silicon run Wan? Yes, via MLX-based inference ports. A Mac Studio M4 Max (128 GB unified memory) runs Wan 14B without quantization tricks — unified memory means the 54 GB requirement isn’t a problem. Per-step generation speed is slower than an equivalent VRAM NVIDIA setup, but the experience is polished and free of CUDA compatibility issues. See the Ollama MLX guide for the general Apple Silicon local AI setup.

What about LTX Video or HunyuanVideo? LTX Video 2.3 is faster per frame and runs on 8 GB VRAM more comfortably than Wan. HunyuanVideo produces excellent quality but needs 47 GB at full precision (drops to ~8 GB with FP8 quantization). Wan 2.2 14B sits in the middle: heavier than LTX but more configurable than HunyuanVideo for local inference. If you’re running an 8 GB GPU and want quality, LTX Video 2.3 is worth testing alongside Wan 1.3B.

Sources

Last updated June 3, 2026. GPU prices and model releases change frequently; verify current rates before purchasing.

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