Best AI Models for NVIDIA GeForce RTX 3080 Ti (12.0GB)
12 GB is the sweet spot for entry into local AI. It runs 7B–13B models at good quality quantizations, making it a practical and affordable starting point for running LLMs on your own hardware.
This memory tier, common on GPUs like the RTX 3060 12GB, is surprisingly capable for local AI. You can run Llama 3 8B, Mistral 7B, and similar 7B models at Q4_K_M quantization with decent token generation speed. Smaller models like Phi 3 Mini (3.8B) run at Q6 or Q8 with room to spare. Reaching up to 13B models is possible at Q2–Q3 quantization, though quality trade-offs become more noticeable.
Runs Well
- 7B models at Q4_K_M quality
- Small models (3B–4B) at Q5–Q8
- Chat and coding assistants for everyday use
Challenging
- 13B models only at Q2–Q3 (lower quality)
- 14B+ models do not fit
- Context windows limited for 7B+ models
What LLMs Can NVIDIA GeForce RTX 3080 Ti Run?
80 models · 9 excellent · 10 good
Showing compatibility for NVIDIA GeForce RTX 3080 Ti
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·97.2 t/s tok/s·8K ctx·GOOD FIT | Q4_K_M | 6.1 GB | 97.2 t/s | 8K | GOOD FIT | A66 |
Q4_K_M·107.4 t/s tok/s·41K ctx·FAIR FIT | Q4_K_M | 5.5 GB | 107.4 t/s | 41K | FAIR FIT | B61 |
IQ2_XXS·55.9 t/s tok/s·262K ctx·FAIR FIT | IQ2_XXS | 10.6 GB | 55.9 t/s | 262K | FAIR FIT | B60 |
Q4_K_M·111.9 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 5.3 GB | 111.9 t/s | 131K | FAIR FIT | B59 |
Q4_K_M·98.7 t/s tok/s·33K ctx·GOOD FIT | Q4_K_M | 6.0 GB | 98.7 t/s | 33K | GOOD FIT | A65 |
Q4_K_M·111.5 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 5.3 GB | 111.5 t/s | 131K | FAIR FIT | B59 |
Q4_K_M·118.8 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 5.0 GB | 118.8 t/s | 33K | FAIR FIT | B57 |
Q4_K_M·103.1 t/s tok/s·66K ctx·FAIR FIT | Q4_K_M | 5.8 GB | 103.1 t/s | 66K | FAIR FIT | B63 |
Q4_K_M·87.5 t/s tok/s·8K ctx·GOOD FIT | Q4_K_M | 6.8 GB | 87.5 t/s | 8K | GOOD FIT | A72 |
Q4_K_M·107.4 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 5.5 GB | 107.4 t/s | 131K | FAIR FIT | B61 |
Q4_K_M·110.0 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 5.4 GB | 110.0 t/s | 131K | FAIR FIT | B60 |
Q4_K_M·110.0 t/s tok/s·131K ctx·FAIR FIT | Q4_K_M | 5.4 GB | 110.0 t/s | 131K | FAIR FIT | B60 |
Q4_K_M·120.5 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 4.9 GB | 120.5 t/s | 33K | FAIR FIT | B56 |
Q4_K_M·109.4 t/s tok/s·16K ctx·FAIR FIT | Q4_K_M | 5.4 GB | 109.4 t/s | 16K | FAIR FIT | B60 |
IQ2_M·55.4 t/s tok/s·262K ctx·FAIR FIT | IQ2_M | 10.7 GB | 55.4 t/s | 262K | FAIR FIT | B56 |
Q4_K_M·56.6 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 10.5 GB | 56.6 t/s | 33K | FAIR FIT | B63 |
NVIDIA GeForce RTX 3080 Ti Specifications
- Brand
- NVIDIA
- Architecture
- Ampere
- Compute Capability
- 8.6 (CUDA SM version)
- VRAM
- 12.0 GB GDDR6X
- Memory Bandwidth
- 912.4 GB/s
- CUDA Cores
- 10,240
- Tensor Cores
- 320
- FP16 Performance
- 68.20 TFLOPS
- TDP
- 350W
- Release Date
- 2021-06-03
- MSRP
- $1,199
Get Started
GPUs to Consider Over NVIDIA GeForce RTX 3080 Ti
Similar GPUs and upgrades with more VRAM or higher bandwidth for AI
NVIDIA GeForce RTX 3090 Ti
NVIDIA · Ampere
NVIDIA GeForce RTX 4090
NVIDIA · Ada Lovelace
AMD Radeon RX 7900 XTX
AMD · RDNA 3
NVIDIA GeForce RTX 5080
NVIDIA · Blackwell
NVIDIA GeForce RTX 3090
NVIDIA · Ampere
NVIDIA GeForce RTX 5070 Ti
NVIDIA · Blackwell
Frequently Asked Questions
- Can NVIDIA GeForce RTX 3080 Ti run Gemma 3 12B IT?
Yes, the NVIDIA GeForce RTX 3080 Ti with 12 GB can run Gemma 3 12B IT, Gemma 4 12B IT, Llama 2 13B Chat HF, and 1056 other models. 44 models run at excellent quality, and 176 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.
- Is NVIDIA GeForce RTX 3080 Ti good for AI?
The NVIDIA GeForce RTX 3080 Ti has 12 GB of GDDR6X, making it solid for running local AI models. It supports 220 models at good quality or better. With 912.4 GB/s memory bandwidth, it delivers fast token generation speeds. It's a practical entry point — ideal for 7B models like Llama 3 8B and Mistral 7B.
- How many parameters can NVIDIA GeForce RTX 3080 Ti handle?
With 12 GB, the NVIDIA GeForce RTX 3080 Ti supports models from 3B to 13B parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 20B parameters. 7B models fit well at Q4–Q5, with room for context. Larger 13B models need Q3 or lower.
- What quantization should I use on NVIDIA GeForce RTX 3080 Ti?
For the best balance of quality and speed on the NVIDIA GeForce RTX 3080 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 3080 Ti for AI inference?
With 912.4 GB/s memory bandwidth, the NVIDIA GeForce RTX 3080 Ti achieves approximately 132 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~66 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.
tok/s = (912.4 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 3080 Ti
~74 tok/s~72 tok/s~69 tok/s~62 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 3080 Ti?
The top-rated models for the NVIDIA GeForce RTX 3080 Ti are Gemma 3 12B IT, Gemma 4 12B IT, Llama 2 13B Chat HF. 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 3080 Ti need?
The NVIDIA GeForce RTX 3080 Ti has a TDP of 350 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 750 W PSU or larger. At this power level, a high-airflow case matters: aim for at least two front intake fans and one rear exhaust, with tidy cabling so hot air isn't trapped around the card. LLM inference sustains full GPU load continuously — longer and more consistently than most gaming workloads — so also make sure your CPU cooler can keep up under combined load.