Local AI suitability
The desktop RTX 4060 is one of the most popular mainstream GPUs and, with 8 GB of VRAM, a sensible baseline for local AI. It runs the whole 7B–8B class at 4-bit entirely in VRAM with room for context, and handles 12–14B models with lighter quantization.
A good balance of price and capability. 8 GB comfortably holds 7B/8B models at Q4–Q5. Some 12B–14B models fit at aggressive quantization, though headroom for large context shrinks. CPU offload is available for larger models thanks to typical 16–32 GB system RAM.
Recommended model sizes
- 7B (Q5/Q6)
- 8B (Q4/Q5)
- 12B (Q3/Q4)
- 14B (Q3)
Recommended quantizations
- Q4_K_M / Q5_K_M (GGUF)
- GPTQ Int4
- AWQ 4-bit
Limitations
- 8 GB limits larger 14B+ models to tight quantization or partial offload
- 128-bit bus is modest; very long contexts degrade speed
How it compares
The step up to an RTX 4070 (12 GB) is the single most impactful upgrade for local AI at this tier — it unlocks comfortable 12B–14B use and larger contexts. The RTX 3060 12 GB offers more VRAM for less bandwidth.
Compatible models
Model pages below include per-quantization memory estimates and FAQs.
FAQ
Is 8 GB VRAM enough for local LLMs?
Yes for the 7B–8B class at 4-bit, which is where the best quality-per-GB lives today. It is also workable for 12B–14B at aggressive quantization. Beyond that you need more VRAM or CPU offload.
RTX 4060 vs RTX 3060 12 GB for AI?
The 3060's 12 GB lets you hold bigger models (e.g. 12B–13B at Q4); the 4060 is faster per token thanks to newer architecture and higher bandwidth, but its 8 GB is the hard ceiling. Choose the 3060 for capacity, the 4060 for speed on 7B-class models.
What can I expect for tokens per second?
A Q4 7B–8B model typically runs around 30–60 tok/s on a 4060 with llama.cpp or Ollama; AWQ/GPTQ through vLLM can be similar or faster for batchy workloads but uses more VRAM.
Does context length matter on 8 GB?
Yes. KV cache grows with context. An 8K context on a Q4 8B model can add over 1 GB; at 32K it can push you out of VRAM entirely. Keep context modest or offload to RAM.