Best LLMs for 16GB VRAM in 2026 — ranked by measured file sizes
Every pick below comfortably fits 16GB VRAM at the listed quant with 8K context. Ranking = largest model (more capable) that still fits, ties broken by real download counts. Sizes are measured GGUF files from Hugging Face, updated daily — not formula estimates.
16GB runs 24-32B models at Q3/Q4 and most 12-14B models at near-lossless Q8. Long contexts (32K+) start to fit here if you drop a quant step.
| Model | Params | Best quant that fits | VRAM needed | Downloads |
|---|---|---|---|---|
| MiniCPM-V-4.7-35B-A3B-i1 | 35B | IQ2_S | 14.3 GB | 12k |
| granite-4.2-30b | 30B | Q2_K | 14.5 GB | 230k |
| Xing4.0-29B-A4B | 29B | IQ2_M | 11.3 GB | 64k |
| Qwen3.8-27B | 27B | UD-IQ3_XXS | 14.1 GB | 5751k |
| Ternary-Bonsai-2-27B | 27B | Q2_0 | 10.1 GB | 2336k |
| gemma-4-26B-A4B-it | 25B | UD-IQ3_XXS | 14.4 GB | 963k |
| mistralai_Mistral-Small-3.2-24B-Instruct-2506 | 24B | IQ3_M | 14.3 GB | 42k |
| LFM2.5-24B-A2B-Turbo-Brilliance-50X-NEO-MAX-Imatrix | 24B | IQ3_M | 14.1 GB | 7k |
| Mistral-Small-3.2-24B-Instruct-2506 | 24B | Q3_K_S | 14.0 GB | 57k |
| Qwen3-14B | 14B | Q5_K_M | 14.1 GB | 246k |
| Ministral-3-14B-Instruct-2512 | 14B | Q5_K_M | 13.0 GB | 30k |
| Humanizer-12B | 12B | Q6_K | 13.2 GB | 4k |
Leaves ~8% headroom for fragmentation. Long contexts (>8K) grow the KV cache — open the model page for exact math.
How we pick
A model qualifies when its measured GGUF file plus KV cache plus runtime overhead stays under 16GB x 0.92. We choose the largest such quant (bigger quant = less degradation), then rank models by parameter count — at 16GB VRAM the best model you can run is almost always the largest one that still fits. MoE models count their total size (all experts live in memory), not just active params.