Can I Run Wan 2.1 14B 720p Video AI on RTX 4090 24GB? VRAM & Offload Guide
Model weights, initial KV-cache, and CUDA context buffers.
Available safety margin for OS compositing and context expansion.
Throughput determined by memory bus bandwidth.
Hardware Physics & Bottleneck Analysis
Wan 2.1 14B is a spatio-temporal Diffusion Transformer (DiT) coupled with a multilingual 5.6B `UMT5-XXL` encoder and a causal 3D Variational Autoencoder (`Wan-VAE`). During text-to-video (T2V) or image-to-video (I2V) synthesis at 1280x720x81 frames, two distinct memory cliffs occur on a 24GB card: first, holding both UMT5-XXL (`~11.4 GB` in FP16) and the 14B DiT (`~28.4 GB` in BF16) simultaneously during prompt conditioning; second, the final 3D spatio-temporal VAE decoding pass where latent spatio-temporal feature maps expand by 8x8x4. Quantizing the DiT backbone to native Ada Lovelace `fp8_e4m3fn` (`~14.2 GB`) or GGUF `Q4_K_M` (`~9.8 GB`), unloading UMT5-XXL immediately after prompt encoding, and enabling tiled 3D VAE decoding keeps the entire denoising loop inside 19.40 GB on the RTX 4090.
In ComfyUI-WanVideoWrapper or native `ComfyUI`, load `wan2.1_t2v_14B_fp8_e4m3fn.safetensors` with `umt5_xxl_fp8_e4m3fn_scaled.safetensors`, enable `SageAttention` (`--use-sage-attention`), and set `WanVideoBlockSwap` to swap 10 of the 40 DiT blocks to pinned DDR5 RAM when generating 720p 81-frame sequences.
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Frequently Asked Questions
Why does Wan 2.1 14B crash with CUDA OOM at 99% completion on RTX 4090?
Crashing at 98%–99% means the 40 DiT denoising steps succeeded, but the final 3D Causal VAE decode step ran out of memory converting latents into RGB frames. Replace the standard `VAE Decode` node with `VAE Decode (Tiled)` (tile size 256x256, temporal overlap 8) to eliminate the end-of-render OOM spike.
How much faster is TeaCache + SageAttention for Wan 2.1 14B on RTX 4090?
Combining `SageAttention` (INT8/FP8 attention kernels on Ada Tensor Cores) with `TeaCache` (`rel_l1_thresh = 0.15`) cuts 720p 81-frame generation time on an RTX 4090 from ~310 seconds down to ~135–150 seconds with negligible motion quality loss.
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