Local AI suitability
The RTX 3060 12 GB has become the classic budget entry point for local AI. Unusual for its price class, it carries 12 GB of VRAM — enough to hold 7B–8B models at comfortable quantization and 13B-class models at 4-bit entirely on the card.
Very good for its price, especially second-hand. Bandwidth is Ampere-era, so tokens per second trail Ada cards, but capacity-first buyers can run 13B Q4 models that 8 GB cards cannot hold at all.
Recommended model sizes
- 7B (Q6/Q8)
- 8B (Q5)
- 12B (Q4/Q5)
- 13B (Q4)
Recommended quantizations
- Q4_K_M / Q5_K_M (GGUF)
- GPTQ Int4
Limitations
- 360 GB/s bandwidth caps speed; expect roughly half a 4070's throughput
- Ampere lacks newer instruction efficiencies; power efficiency is lower
How it compares
Versus the RTX 4060 (8 GB): the 3060 trades speed for 4 GB more VRAM. If you want to run 12B–13B models, choose the 3060; if you mostly run 7B–8B and want speed, the 4060 is faster and more efficient.
Compatible models
Model pages below include per-quantization memory estimates and FAQs.
FAQ
Is the RTX 3060 12 GB still worth buying for AI?
Yes, particularly used. It is often the cheapest way to get 12 GB of VRAM, which matters more than raw speed for fitting 13B-class models.
What speed can I expect?
A Q4 7B model typically runs 25–40 tok/s; a Q4 13B around 15–25 tok/s through llama.cpp. Numbers vary with CPU pairing and settings.
Can it run vision or multimodal models?
Yes, smaller multimodal models (vision encoders plus a 7B–8B language model) fit at 4-bit quantization within 12 GB.
3060 12 GB vs 4060 Ti 16 GB?
The 4060 Ti 16 GB adds capacity and speed but costs much more. If budget is fixed, the used 3060 12 GB remains the value pick; if you can spend more, the 16 GB variant is the better long-term card.