RTX 3060 12GB vs RTX 4060 Ti 16GB for Local LLMs
The RTX 4060 Ti 16GB holds bigger models (16.0 GB vs 12.0 GB), and the RTX 3060 12GB generates tokens about 25% faster. Below: speeds on seven popular models, the models only one card can run, and our verdict.
Specs that matter for AI
| RTX 3060 12GB | RTX 4060 Ti 16GB | |
|---|---|---|
| Memory | 12 GB | 16 GB |
| GPU-usable memory | 12.0 GB | 16.0 GB |
| Bandwidth | 360 GB/s | 288 GB/s |
| Architecture | Ampere | Ada Lovelace |
| Released | 2021-02 | 2023-07 |
| Llama 3.1 8B Q4_K_M | ~43 tok/s | ~34 tok/s |
Speed and fit by model
Q4_K_M (or native format) with 8K context.
| Model | VRAM | RTX 3060 12GB | RTX 4060 Ti 16GB |
|---|---|---|---|
| Llama 3.1 8B | 6.3 GB | Fits ~43 tok/s | Fits ~34 tok/s |
| Qwen 3 14B | 10.5 GB | Fits ~24 tok/s | Fits ~19 tok/s |
| gpt-oss-20b | 13.9 GB | 81% on GPU ~45 tok/s | Fits ~64 tok/s |
| Qwen 3 30B-A3B | 18.8 GB | 60% on GPU ~30 tok/s | 80% on GPU ~38 tok/s |
| Qwen 3 32B | 21.4 GB | 53% on GPU ~3.5 tok/s | 70% on GPU ~4.4 tok/s |
| Llama 3.3 70B | 43.2 GB | Too small | 35% on GPU ~1.3 tok/s |
| gpt-oss-120b | 62.0 GB | Too small | Too small |
Verdict
The 4060 Ti 16GB holds bigger models: 16 GB fits 24B models at Q4_K_M and 14B models at Q6_K with long context, which the 3060 cannot. Token speed is close because both cards have narrow memory buses (360 vs 288 GB/s), and the 3060 is actually a little faster on models that fit in 12 GB. Buy the 3060 12GB on a tight budget and the 4060 Ti 16GB if you want the extra headroom.
Which should you buy?
Choose the RTX 3060 12GB if…
- You are on a tight budget and can find one used.
- You mainly run 8–14B models.
- You want the slightly faster card for models that fit in 12 GB.
Choose the RTX 4060 Ti 16GB if…
- You want to run gpt-oss-20b or 24B models at Q4.
- You need longer contexts on 14B models.
- You care about low power use and a new card with warranty.
Best quality and longest context on each card
The highest-quality quantization that fits fully at 8K context, and the longest context that fits at Q4_K_M with an FP16 cache.
| Model | RTX 3060 12GB: best quant | RTX 4060 Ti 16GB: best quant | RTX 3060 12GB: max context | RTX 4060 Ti 16GB: max context |
|---|---|---|---|---|
| Qwen 3.5 9B | Q8_0 | Q8_0 | 164K | 256K |
| Qwen 3 14B | Q4_K_M | Q6_K | 13K | 37K |
| gpt-oss-20b | Q3_K_M | MXFP4 (official) | – | 55K |
| Qwen 3.6 27B | Q2_K | IQ4_XS | – | – |
| Qwen 3 32B | Does not fit | Q2_K | – | – |
| Llama 3.3 70B | Does not fit | Does not fit | – | – |
Image generation
Best weight precision for image models with model swapping, at each model's default resolution. Image generation is also limited by compute, so the card with more tensor-core throughput finishes each image faster even when both fit.
| Model | RTX 3060 12GB | RTX 4060 Ti 16GB |
|---|---|---|
| SDXL 1.0 | FP16 / BF16 | FP16 / BF16 |
| FLUX.1 [dev] | GGUF Q6_K | FP8 |
| Qwen-Image | Needs offloading | GGUF Q5_K_S |
| Wan 2.2 A14B (T2V / I2V) | Needs offloading | GGUF Q5_K_S |
Models only one card can run
Only RTX 3060 12GB
None
Only RTX 4060 Ti 16GB
Frequently asked questions
Is the RTX 3060 12GB or RTX 4060 Ti 16GB better for local LLMs?
The 4060 Ti 16GB holds bigger models: 16 GB fits 24B models at Q4_K_M and 14B models at Q6_K with long context, which the 3060 cannot. Token speed is close because both cards have narrow memory buses (360 vs 288 GB/s), and the 3060 is actually a little faster on models that fit in 12 GB. Buy the 3060 12GB on a tight budget and the 4060 Ti 16GB if you want the extra headroom.
Which is faster for LLM inference?
The RTX 3060 12GB, by about 25% for token generation on models that fit both (about 43 vs 34 tokens/s on Llama 3.1 8B Q4_K_M).
Which can run bigger models?
The RTX 4060 Ti 16GB, with 16.0 GB usable versus 12.0 GB.