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?
66 models · 6 excellent · 19 good
Showing compatibility for NVIDIA GeForce RTX 3070 Ti
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q3_K_M·56.7 t/s tok/s·FAIR FIT | Q3_K_M | 7.0 GB | 56.7 t/s | — | FAIR FIT | B63 |
Q3_K_M·56.7 t/s tok/s·2K ctx·FAIR FIT | Q3_K_M | 7.0 GB | 56.7 t/s | 2K | FAIR FIT | B63 |
Q4_K_M·136.3 t/s tok/s·262K ctx·FAIR FIT | Q4_K_M | 2.9 GB | 136.3 t/s | 262K | FAIR FIT | B51 |
Q3_K_M·56.7 t/s tok/s·FAIR FIT | Q3_K_M | 7.0 GB | 56.7 t/s | — | FAIR FIT | B63 |
Q4_K_M·139.2 t/s tok/s·FAIR FIT | Q4_K_M | 2.8 GB | 139.2 t/s | — | FAIR FIT | B51 |
Q4_K_M·137.8 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 2.9 GB | 137.8 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·137.8 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 2.9 GB | 137.8 t/s | 131K | FAIR FIT | B51 |
Q4_K_M·171.9 t/s tok/s·66K ctx·EASY RUN | Q4_K_M | 2.3 GB | 171.9 t/s | 66K | EASY RUN | C44 |
Q4_K_M·157.5 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 2.5 GB | 157.5 t/s | 131K | FAIR FIT | B46 |
Q4_K_M·186.5 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.1 GB | 186.5 t/s | 131K | EASY RUN | C42 |
Q4_K_M·177.3 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 2.2 GB | 177.3 t/s | 33K | EASY RUN | C43 |
Q4_K_M·181.4 t/s tok/s·16K ctx·EASY RUN | Q4_K_M | 2.2 GB | 181.4 t/s | 16K | EASY RUN | C42 |
IQ4_NL·52.4 t/s tok/s·33K ctx·POOR FIT | IQ4_NL | 7.5 GB | 52.4 t/s | 33K | POOR FIT | C36 |
Q4_K_M·53.9 t/s tok/s·8K ctx·POOR FIT | Q4_K_M | 7.3 GB | 53.9 t/s | 8K | POOR FIT | C44 |
IQ2_M·51.8 t/s tok/s·131K ctx·POOR FIT | IQ2_M | 7.6 GB | 51.8 t/s | 131K | POOR FIT | C33 |
NVIDIA GeForce RTX 3070 Ti Specifications
- Brand
- NVIDIA
- Architecture
- Ampere
- Compute Capability
- 8.6 (CUDA SM version)
- 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, Qwen1.5 7B, and 964 other models. 88 models run at excellent quality, and 302 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 390 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~66 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, Qwen1.5 7B. 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.
- What power supply and cooling does NVIDIA GeForce RTX 3070 Ti need?
The NVIDIA GeForce RTX 3070 Ti has a TDP of 290 W. A good rule of thumb is to provide at least double the GPU's TDP to cover the rest of the system — that means a 650 W PSU or larger. A mid-tower case with one intake and one rear exhaust is usually sufficient. Keep dust filters clean, as sustained inference generates continuous heat rather than the brief spikes typical of gaming.