Best local AI models for 20GB VRAM
Ranked by what actually fits at 32K context, computed from real file bytes.
A 20GB card gives you about 18.60 GiB to work with after driver overhead. 16 indexed models fit at 32K context — the largest being lingbot-world-v2-14b-causal-fast at 18.5B parameters in Q6_K.
From the file· fit from summed bytesFrom the file· KV per layer
Fits in 20GB at 32K context
largest quantization that fits, per model
| Model | Modality | Best quant | Params○ | Total◐ | Headroom◐ |
|---|---|---|---|---|---|
| Wan2.2-Animate-14B | video generation | Q8_0 | 17.3B | 18.27 GiB | 0.33 GiB |
| Wan2.1-I2V-14B-480P | video generation | Q8_0 | 16.4B | 17.73 GiB | 0.87 GiB |
| Bernini-R | video generation | Q6_K | 14.3B | 12.02 GiB | 6.58 GiB |
| Wan2.2-Distill-Models | video generation | Q8_0 | 14.3B | 15.19 GiB | 3.41 GiB |
| Wan2.2-TI2V-5B | video generation | Q8_0 | 5.0B | 5.87 GiB | 12.73 GiB |
| Wan2.1-T2V-14B | video generation | Q8_0 | 14.3B | 15.62 GiB | 2.98 GiB |
| Wan-Dancer-14B | video generation | Q3_K_M | 17.2B | 16.66 GiB | 1.94 GiB |
| Wan2.1-I2V-14B-720P | video generation | Q8_0 | 16.4B | 17.73 GiB | 0.87 GiB |
| Wan2.1-VACE-14B | video generation | Q8_0 | 17.3B | 18.22 GiB | 0.38 GiB |
| Wan2.2-S2V-14B | video generation | Q6_K | 16.3B | 15.94 GiB | 2.66 GiB |
| Wan2.1-FLF2V-14B-720P | video generation | Q8_0 | 16.4B | 17.74 GiB | 0.86 GiB |
| JoyAI-Echo | video generation | Q4_K_M | 12.2B | 15.46 GiB | 3.14 GiB |
| HunyuanVideo-1.5 | video generation | Q8_0 | 8.3B | 9.22 GiB | 9.38 GiB |
| Wan2.2-TI2V-5B-Turbo | video generation | Q8_0 | 5.0B | 5.87 GiB | 12.73 GiB |
| SkyReels-V2-DF-14B-540P | video generation | Q8_0 | 14.3B | 15.19 GiB | 3.41 GiB |
| lingbot-world-v2-14b-causal-fast | video generation | Q6_K | 18.5B | 15.27 GiB | 3.33 GiB |
This page models a generic 20GB accelerator, so it answers what fits rather than how fast it runs. For tokens per second you need a specific card — pick one from hardware, where bandwidth is known.