Hardware compatibility · 5 open-weight models
Which open-weight LLMs can your hardware run?
VRAM estimates are computed as parameters × bytes-per-quant × 1.2 overhead, picking the best available quantization for your budget. Apple Silicon figures apply a 0.75 usable-memory factor since the OS reserves headroom in unified memory. CPU-only inference is supported but expect slower generation. Methodology →
Enter your VRAM
Pick a preset rig or type a custom VRAM budget to see which models fit — ranked by intelligence, speed, and price.
GPU
5 rigs12 GB
RTX 3060 (12GB)
Runs ~0 of 5 models locally.
See ranked models →16 GB
RTX 4060 Ti (16GB)
Runs ~0 of 5 models locally.
See ranked models →24 GB
RTX 4090 (24GB)
Runs ~1 of 5 models locally.
See ranked models →24 GB
RTX 3090 (24GB)
Runs ~1 of 5 models locally.
See ranked models →48 GB
RTX A6000 (48GB)
Runs ~1 of 5 models locally.
See ranked models →Apple Silicon
2 rigsMulti-GPU
1 rigsCPU
1 rigsCPU-only inference is correct but slow. Expect 1–5 tokens/sec on a modern desktop CPU.