This article was originally published on runaihome.com
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 12GB | RTX 5060 Ti 16GB | RTX 4090 24GB | |
|---|---|---|---|
| Best for | Wan 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 min | 3–5 min |
| The catch | VRAM ceiling blocks 720p | 128-bit bus limits bandwidth vs. 4070 | Supply-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:
- The video diffusion transformer itself: ~14 GB in FP8, ~28 GB in FP16
- 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.
| GPU | VRAM | 480p (5-sec clip) | 720p (5-sec clip) |
|---|---|---|---|
| H100 SXM | 80 GB | 85 sec | 284 sec |
| A100 SXM | 80 GB | 170 sec | 523 sec |
| A40 | 48 GB | 501 sec | 1,083 sec |
| RTX 4090 | 24 GB | 281 sec | OOM |
| RTX 3090 | 24 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 ge
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