Best local AI models for 16GB VRAM
Ranked by what actually fits at 32K context, computed from real file bytes.
A 16GB card gives you about 14.88 GiB to work with after driver overhead. 15 indexed models fit at 32K context — the largest being lingbot-world-v2-14b-causal-fast at 18.5B parameters in Q4_K_S.
From the file· fit from summed bytesFrom the file· KV per layer
Fits in 16GB at 32K context
largest quantization that fits, per model
| Model | Modality | Best quant | Params○ | Total◐ | Headroom◐ |
|---|---|---|---|---|---|
| Wan2.2-Animate-14B | video generation | Q6_K | 17.3B | 14.44 GiB | 0.44 GiB |
| Wan2.1-I2V-14B-480P | video generation | Q6_K | 16.4B | 14.09 GiB | 0.79 GiB |
| Bernini-R | video generation | Q6_K | 14.3B | 12.02 GiB | 2.86 GiB |
| Wan2.2-Distill-Models | video generation | Q6_K | 14.3B | 12.02 GiB | 2.86 GiB |
| Wan2.2-TI2V-5B | video generation | Q8_0 | 5.0B | 5.87 GiB | 9.01 GiB |
| Wan2.1-T2V-14B | video generation | Q6_K | 14.3B | 12.45 GiB | 2.43 GiB |
| Wan2.1-I2V-14B-720P | video generation | Q6_K | 16.4B | 14.09 GiB | 0.79 GiB |
| Wan2.1-VACE-14B | video generation | Q6_K | 17.3B | 14.36 GiB | 0.52 GiB |
| Wan2.2-S2V-14B | video generation | Q5_K_M | 16.3B | 14.81 GiB | 0.07 GiB |
| Wan2.1-FLF2V-14B-720P | video generation | Q6_K | 16.4B | 14.09 GiB | 0.79 GiB |
| JoyAI-Echo | video generation | Q8_0 | 12.2B | 13.29 GiB | 1.59 GiB |
| HunyuanVideo-1.5 | video generation | Q8_0 | 8.3B | 9.22 GiB | 5.66 GiB |
| Wan2.2-TI2V-5B-Turbo | video generation | Q8_0 | 5.0B | 5.87 GiB | 9.01 GiB |
| SkyReels-V2-DF-14B-540P | video generation | Q6_K | 14.3B | 12.02 GiB | 2.86 GiB |
| lingbot-world-v2-14b-causal-fast | video generation | Q4_K_S | 18.5B | 14.48 GiB | 0.40 GiB |
This page models a generic 16GB 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.