AI Image & Video VRAM Calculator
Scientific VRAM analysis based on Config files for Flux.2, Wan 2.1, and Hunyuan
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Common VRAM Questions
Yes, but only with Quantization!
Originally, Flux.2 (32B) requires ~37GB VRAM (FP16/FP8), which is impossible on consumer cards. However, using the Q4_K_M (4-bit) quantization shown in this calculator, the memory usage drops to ~22.7 GB.
It is a tight fit, but it runs natively on 24GB cards like the RTX 4090 or 7900 XTX without slow CPU offloading.
Originally, Flux.2 (32B) requires ~37GB VRAM (FP16/FP8), which is impossible on consumer cards. However, using the Q4_K_M (4-bit) quantization shown in this calculator, the memory usage drops to ~22.7 GB.
It is a tight fit, but it runs natively on 24GB cards like the RTX 4090 or 7900 XTX without slow CPU offloading.
CogVideoX-5B is your best choice.
According to our calculator, CogVideoX uses approximately 11.3 GB VRAM at 720p (Q4_K_M). This fits onto a 12GB RTX 3060 or 4070, leaving just enough headroom for the OS.
Note: Close your web browser to free up VRAM before running it.
According to our calculator, CogVideoX uses approximately 11.3 GB VRAM at 720p (Q4_K_M). This fits onto a 12GB RTX 3060 or 4070, leaving just enough headroom for the OS.
Note: Close your web browser to free up VRAM before running it.
Mochi 1 is significantly lighter (~30% less VRAM).
For a standard 49-frame video, Wan 2.1 requires about 17.7 GB (demanding a 24GB GPU), whereas Mochi 1 only needs around 12.2 GB. This makes Mochi 1 capable of running on 16GB cards (like the 4060 Ti 16GB) where Wan 2.1 would fail or run extremely slow.
For a standard 49-frame video, Wan 2.1 requires about 17.7 GB (demanding a 24GB GPU), whereas Mochi 1 only needs around 12.2 GB. This makes Mochi 1 capable of running on 16GB cards (like the 4060 Ti 16GB) where Wan 2.1 would fail or run extremely slow.
Yes, absolutely.
With Q4_K_M quantization, the total VRAM usage is around 6.9 GB for standard 1024x1024 generation.
This leaves over 1GB of headroom, so it runs smoothly on 8GB cards (RTX 3060 Ti, 4060) without offloading.
With Q4_K_M quantization, the total VRAM usage is around 6.9 GB for standard 1024x1024 generation.
This leaves over 1GB of headroom, so it runs smoothly on 8GB cards (RTX 3060 Ti, 4060) without offloading.