What can you run with 16GB of RAM?
With 16GB RAM 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 ~8.1 tok/s on typical hardware of this size.
Typical machines: MacBook Air M4 · Mid-range Windows laptops · Mac mini (base). After the operating system takes its share, about 14 GB is free for a model.
Having the memory is not the same as having the speed
16GB RAM 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 16GB RAM span 51–153 GB/s, so the fastest is about 3.0× quicker than the slowest with identical capacity. The table quotes a DDR5-5600; pick yours below to see the difference. Why bandwidth and not the processor →
Models that run on 16GB RAM in 2026
| Model↕ | Size↕ | Memory↑ | Est. speed↕ | Downloads↕ | Feels like |
|---|---|---|---|---|---|
| LFM2.5-230MLiquid Ai | 230M | 808 MBRuns well | ~381 tok/s | 58,011 | Fast |
| MiniCPM5-1BOpenBMB | 1.1B | 1.3 GBRuns well | ~90 tok/s | 667,154 | Fast |
| MiniCPM5-2BOpenBMB | 2.5B | 2.3 GBRuns well | ~37 tok/s | 206,774 | Fast |
| LFM2.5-2.6BLiquid Ai | 2.7B | 2.5 GBRuns well | ~35 tok/s | 98,713 | Fast |
| LFM2.5-VL-3BLiquid Ai | 3.1B | 2.5 GBRuns well | ~35 tok/s | 31,296 | Fast |
| North-Micro-Vision-InstructCohere | 2.5B | 2.5 GBRuns well | ~39 tok/s | 120,354 | Fast |
| granite-4.2-3bIbm | 3.7B | 3.1 GBRuns well | ~26 tok/s | 32,017 | Fast |
| Mage-VLMicrosoft | 4.7B | 3.9 GBRuns well | ~22 tok/s | 29,690 | Comfortable |
| LFM2.5-8B-A1BLiquid Ai · MoE | 8.5B | 5.9 GBRuns well | ~96 tok/s1B active | 80,632 | Fast |
| Ling-3.0-tinyInclusionai · MoE | 7.9B | 6.2 GBRuns well | ~61 tok/s1.6B active | 25,727 | Fast |
| granite-4.2-8bIbm | 8.8B | 6.6 GBRuns well | ~11 tok/s | 52,946 | Comfortable |
| Ornith-1.5-9BDeepreinforce | 9.7B | 6.9 GBRuns well | ~10 tok/s | 453,339 | Comfortable |
| gemma-4-12B-itGoogle DeepMind | 12B | 9.6 GBRuns well | ~8.1 tok/s | 2,958,440 | Comfortable |
Sorted by memory, smallest first — click any column to change it.
Step up to 24GB RAM and 4 more models come within reach
Including Qwen3.8-27B (28B), diffusiongemma-26B-A4B-it (26B) and Muse-Glimmer-30B (30B) — around ~4.7 tok/s on a typical machine that size.
See everything 24GB RAM runs →All figures at Q4_K_M quantization, 4K context. “Memory” includes the model, its context cache and runtime overhead. Speed is calculated, not benchmarked: 90 GB/s × 0.65 ÷ bytes read per token, assuming a DDR5-5600. 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.