What can you run on a 12GB graphics card?
With 12GB VRAM you can run 13 of the 51 open-source models we track — up to about 12B parameters at Q4 quantization. The most popular that fits is gemma-4-12B-it, needing 9.6 GB and generating around ~45 tok/s on typical hardware of this size.
Typical machines: RTX 4070 · RTX 5070 · RTX 3060 12GB · RX 7700 XT. After the operating system takes its share, about 12 GB is free for a model.
Having the memory is not the same as having the speed
12GB VRAM decides whether a model loads. How fast it answers is decided by memory bandwidth — how quickly your machine can read the model, which it must do once for every word it writes. Machines with 12GB VRAM span 360–672 GB/s, so the fastest is about 1.9× quicker than the slowest with identical capacity. The table quotes a RTX 4070; pick yours below to see the difference. Why bandwidth and not the processor →
Models that run on 12GB VRAM in 2026
| Model↕ | Size↕ | Memory↑ | Est. speed↕ | Downloads↕ | Feels like |
|---|---|---|---|---|---|
| LFM2.5-230MLiquid Ai | 230M | 808 MBRuns well | ~2136 tok/s | 58,011 | Fast |
| MiniCPM5-1BOpenBMB | 1.1B | 1.3 GBRuns well | ~502 tok/s | 667,154 | Fast |
| MiniCPM5-2BOpenBMB | 2.5B | 2.3 GBRuns well | ~210 tok/s | 206,774 | Fast |
| LFM2.5-2.6BLiquid Ai | 2.7B | 2.5 GBRuns well | ~196 tok/s | 98,713 | Fast |
| LFM2.5-VL-3BLiquid Ai | 3.1B | 2.5 GBRuns well | ~196 tok/s | 31,296 | Fast |
| North-Micro-Vision-InstructCohere | 2.5B | 2.5 GBRuns well | ~218 tok/s | 120,354 | Fast |
| granite-4.2-3bIbm | 3.7B | 3.1 GBRuns well | ~146 tok/s | 32,017 | Fast |
| Mage-VLMicrosoft | 4.7B | 3.9 GBRuns well | ~121 tok/s | 29,690 | Fast |
| LFM2.5-8B-A1BLiquid Ai · MoE | 8.5B | 5.9 GBRuns well | ~538 tok/s1B active | 80,632 | Fast |
| Ling-3.0-tinyInclusionai · MoE | 7.9B | 6.2 GBRuns well | ~340 tok/s1.6B active | 25,727 | Fast |
| granite-4.2-8bIbm | 8.8B | 6.6 GBRuns well | ~61 tok/s | 52,946 | Fast |
| Ornith-1.5-9BDeepreinforce | 9.7B | 6.9 GBRuns well | ~57 tok/s | 453,339 | Fast |
| gemma-4-12B-itGoogle DeepMind | 12B | 9.6 GBRuns well | ~45 tok/s | 2,958,440 | Fast |
Sorted by memory, smallest first — click any column to change it.
All figures at Q4_K_M quantization, 4K context. “Memory” includes the model, its context cache and runtime overhead. Speed is calculated, not benchmarked: 504 GB/s × 0.65 ÷ bytes read per token, assuming a RTX 4070. Scale it by your own machine's bandwidth from the list above. Mixture-of-experts models read only their active parameters per token, which is why some large ones outrun smaller dense models.
What you'd need more memory for
The next models up, and what they ask for.