Best AI Models for NVIDIA GeForce RTX 3070 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 3070 Ti Run?
18 models · 2 excellent · 7 good
Showing compatibility for NVIDIA GeForce RTX 3070 Ti
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·71.6 t/s tok/s·41K ctx·GREAT FIT | Q4_K_M | 5.5 GB | 71.6 t/s | 41K | GREAT FIT | S85 |
Q4_K_M·64.8 t/s tok/s·8K ctx·GREAT FIT | Q4_K_M | 6.1 GB | 64.8 t/s | 8K | GREAT FIT | S89 |
Q4_K_M·74.9 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.3 GB | 74.9 t/s | 131K | GOOD FIT | A83 |
Q4_K_M·73.6 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.4 GB | 73.6 t/s | 131K | GOOD FIT | A84 |
Q4_K_M·73.4 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.4 GB | 73.4 t/s | 131K | GOOD FIT | A84 |
Q4_K_M·79.2 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 5.0 GB | 79.2 t/s | 33K | GOOD FIT | A78 |
Q4_K_M·80.4 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 4.9 GB | 80.4 t/s | 33K | GOOD FIT | A78 |
Q4_K_M·79.2 t/s tok/s·131K ctx·GOOD FIT | Q4_K_M | 5.0 GB | 79.2 t/s | 131K | GOOD FIT | A78 |
Q8_0·80.5 t/s tok/s·4K ctx·GOOD FIT | Q8_0 | 4.9 GB | 80.5 t/s | 4K | GOOD FIT | A77 |
Q4_K_M·136.8 t/s tok/s·41K ctx·FAIR FIT | Q4_K_M | 2.9 GB | 136.8 t/s | 41K | FAIR FIT | B51 |
Q4_K_M·138.7 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 2.9 GB | 138.7 t/s | 131K | FAIR FIT | B51 |
Q4_K_M·149.8 t/s tok/s·2K ctx·FAIR FIT | Q4_K_M | 2.6 GB | 149.8 t/s | 2K | FAIR FIT | B48 |
Q4_K_M·199.7 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.0 GB | 199.7 t/s | 131K | EASY RUN | C40 |
Q4_K_M·391.5 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 1.0 GB | 391.5 t/s | 2K | EASY RUN | C32 |
Q4_K_M·299.5 t/s tok/s·8K ctx·EASY RUN | Q4_K_M | 1.3 GB | 299.5 t/s | 8K | EASY RUN | C34 |
Q4_K_M·599.1 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 0.7 GB | 599.1 t/s | 131K | EASY RUN | D29 |
NVIDIA GeForce RTX 3070 Ti Specifications
- Brand
- NVIDIA
- Architecture
- Ampere
- VRAM
- 8.0 GB GDDR6X
- Memory Bandwidth
- 608.3 GB/s
- CUDA Cores
- 6,144
- Tensor Cores
- 192
- FP16 Performance
- 43.50 TFLOPS
- TDP
- 290W
- Release Date
- 2021-06-10
- MSRP
- $599
Get Started
GPUs to Consider Over NVIDIA GeForce RTX 3070 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 3070 Ti run Qwen3 8B?
Yes, the NVIDIA GeForce RTX 3070 Ti with 8 GB can run Qwen3 8B, Gemma 2 9B IT, Llama 3.1 8B Instruct, 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 3070 Ti good for AI?
The NVIDIA GeForce RTX 3070 Ti has 8 GB of GDDR6X, making it usable for running local AI models. It supports 252 models at good quality or better. With 608.3 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 3070 Ti handle?
With 8 GB, the NVIDIA GeForce RTX 3070 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 3070 Ti?
For the best balance of quality and speed on the NVIDIA GeForce RTX 3070 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 3070 Ti for AI inference?
With 608.3 GB/s memory bandwidth, the NVIDIA GeForce RTX 3070 Ti achieves approximately 88 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 = (608.3 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 3070 Ti
~72 tok/s~65 tok/s~75 tok/s~74 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 3070 Ti?
The top-rated models for the NVIDIA GeForce RTX 3070 Ti are Qwen3 8B, Gemma 2 9B IT, Llama 3.1 8B Instruct. 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.