Best AI Models for NVIDIA GeForce RTX 3060 Ti (8.0GB)
8 GB is an entry-level tier for local AI. You can run small 7B models at lower quantization levels, which is great for experimenting but comes with quality and speed trade-offs.
With 8 GB, you're limited to smaller models and lower quantization levels, but it's still enough for a meaningful local AI experience. Phi 3 Mini (3.8B) and similar compact models run well at Q4_K_M. For 7B models like Mistral 7B and Llama 3 8B, you'll need Q2_K or Q3_K_M quantization, which reduces output quality. Think of this tier as ideal for learning and experimentation rather than production workloads.
Runs Well
- 3B–4B models at Q4–Q5 quality
- 7B models at Q2–Q3 (usable but reduced quality)
- Quick experiments and learning
Challenging
- 7B models at Q4+ (VRAM too tight)
- Any model above 7B parameters
- Long context windows even with small models
What LLMs Can NVIDIA GeForce RTX 3060 Ti Run?
18 models · 2 excellent · 7 good
Showing compatibility for NVIDIA GeForce RTX 3060 Ti
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·52.8 t/s tok/s·41K ctx·GREAT FIT | Q4_K_M | 5.5 GB | 52.8 t/s | 41K | GREAT FIT | S85 |
Q4_K_M·55.2 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.3 GB | 55.2 t/s | 131K | GOOD FIT | A83 |
Q4_K_M·47.7 t/s tok/s·8K ctx·GREAT FIT | Q4_K_M | 6.1 GB | 47.7 t/s | 8K | GREAT FIT | S89 |
Q4_K_M·54.2 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.4 GB | 54.2 t/s | 131K | GOOD FIT | A84 |
Q4_K_M·54.0 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.4 GB | 54.0 t/s | 131K | GOOD FIT | A84 |
Q4_K_M·58.4 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 5.0 GB | 58.4 t/s | 33K | GOOD FIT | A78 |
Q4_K_M·59.2 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 4.9 GB | 59.2 t/s | 33K | GOOD FIT | A78 |
Q4_K_M·58.4 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.0 GB | 58.4 t/s | 131K | GOOD FIT | A78 |
Q8_0·59.3 t/s tok/s·4K ctx·GOOD FIT | Q8_0 | 4.9 GB | 59.3 t/s | 4K | GOOD FIT | A77 |
Q4_K_M·100.8 t/s tok/s·41K ctx·FAIR FIT | Q4_K_M | 2.9 GB | 100.8 t/s | 41K | FAIR FIT | B51 |
Q4_K_M·102.2 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 2.9 GB | 102.2 t/s | 131K | FAIR FIT | B51 |
Q4_K_M·110.3 t/s tok/s·2K ctx·FAIR FIT | Q4_K_M | 2.6 GB | 110.3 t/s | 2K | FAIR FIT | B48 |
Q4_K_M·147.1 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.0 GB | 147.1 t/s | 131K | EASY RUN | C40 |
Q4_K_M·288.3 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 1.0 GB | 288.3 t/s | 2K | EASY RUN | C32 |
Q4_K_M·220.6 t/s tok/s·8K ctx·EASY RUN | Q4_K_M | 1.3 GB | 220.6 t/s | 8K | EASY RUN | C34 |
Q4_K_M·441.2 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 0.7 GB | 441.2 t/s | 131K | EASY RUN | D29 |
NVIDIA GeForce RTX 3060 Ti Specifications
- Brand
- NVIDIA
- Architecture
- Ampere
- VRAM
- 8.0 GB GDDR6
- Memory Bandwidth
- 448.0 GB/s
- CUDA Cores
- 4,864
- Tensor Cores
- 152
- FP16 Performance
- 32.40 TFLOPS
- TDP
- 200W
- Release Date
- 2020-12-02
- MSRP
- $399
Get Started
GPUs to Consider Over NVIDIA GeForce RTX 3060 Ti
Similar GPUs and upgrades with more VRAM or higher bandwidth for AI
NVIDIA GeForce RTX 5080
NVIDIA · Blackwell
NVIDIA GeForce RTX 3080 Ti
NVIDIA · Ampere
NVIDIA GeForce RTX 5070 Ti
NVIDIA · Blackwell
NVIDIA GeForce RTX 3080
NVIDIA · Ampere
NVIDIA GeForce RTX 4080 SUPER
NVIDIA · Ada Lovelace
NVIDIA GeForce RTX 4080
NVIDIA · Ada Lovelace
Frequently Asked Questions
- Can NVIDIA GeForce RTX 3060 Ti run Qwen3 8B?
Yes, the NVIDIA GeForce RTX 3060 Ti with 8 GB can run Qwen3 8B, Llama 3.1 8B Instruct, Gemma 2 9B IT, and 666 other models. 55 models run at excellent quality, and 197 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.
- Is NVIDIA GeForce RTX 3060 Ti good for AI?
The NVIDIA GeForce RTX 3060 Ti has 8 GB of GDDR6, making it usable for running local AI models. It supports 252 models at good quality or better. With 448.0 GB/s memory bandwidth, it delivers solid token generation speeds. You can run smaller models and experiment with quantized 7B models.
- How many parameters can NVIDIA GeForce RTX 3060 Ti handle?
With 8 GB, the NVIDIA GeForce RTX 3060 Ti supports models from 1B to 7B parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 13B parameters. Smaller 3B–7B models fit at Q3–Q4 quantization.
- What quantization should I use on NVIDIA GeForce RTX 3060 Ti?
For the best balance of quality and speed on the NVIDIA GeForce RTX 3060 Ti, start with Q4_K_M — it preserves ~85% of the original model quality while keeping VRAM usage reasonable. If a model barely fits, drop to Q3_K_M — quality loss is noticeable but still useful for chat. Avoid Q2_K unless you just want to test whether a model works at all.
- How fast is NVIDIA GeForce RTX 3060 Ti for AI inference?
With 448.0 GB/s memory bandwidth, the NVIDIA GeForce RTX 3060 Ti achieves approximately 65 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. Token generation speed scales inversely with model size — smaller models are significantly faster.
tok/s = (448 GB/s ÷ model GB) × efficiency
Smaller models = faster inference. Memory bandwidth is the main bottleneck for token generation speed.
Estimated speed on NVIDIA GeForce RTX 3060 Ti
~53 tok/s~55 tok/s~48 tok/s~54 tok/sReal-world results typically within ±20%. Speed depends on quantization kernel, batch size, and software stack.
- What's the best model for NVIDIA GeForce RTX 3060 Ti?
The top-rated models for the NVIDIA GeForce RTX 3060 Ti are Qwen3 8B, Llama 3.1 8B Instruct, Gemma 2 9B IT. The best choice depends on your use case: coding assistants benefit from code-tuned models, while general chat works well with instruction-tuned models like Llama or Qwen.