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?
200 models · 163 excellent · 8 good
Showing compatibility for NVIDIA GeForce RTX 3080 Ti
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·290.7 t/s tok/s·131K ctx·RUNS GREAT | Q4_K_M | 2.0 GB | 290.7 t/s | 131K | RUNS GREAT | S100 |
Q4_K_M·102.3 t/s tok/s·37K ctx·RUNS GREAT | Q4_K_M | 5.8 GB | 102.3 t/s | 37K | RUNS GREAT | S92 |
IQ2_M·56.3 t/s tok/s·131K ctx·RUNS WELL | IQ2_M | 10.5 GB | 56.3 t/s | 131K | RUNS WELL | A72 |
Q4_K_M·91.8 t/s tok/s·RUNS GREAT | Q4_K_M | 6.5 GB | 91.8 t/s | — | RUNS GREAT | S90 |
Q4_K_M·178.1 t/s tok/s·33K ctx·RUNS GREAT | Q4_K_M | 3.3 GB | 178.1 t/s | 33K | RUNS GREAT | S99 |
Q4_K_M·312.1 t/s tok/s·131K ctx·RUNS GREAT | Q4_K_M | 1.9 GB | 312.1 t/s | 131K | RUNS GREAT | S100 |
Q4_K_M·206.6 t/s tok/s·131K ctx·RUNS GREAT | Q4_K_M | 2.9 GB | 206.6 t/s | 131K | RUNS GREAT | S100 |
Q4_K_M·335.1 t/s tok/s·262K ctx·RUNS GREAT | Q4_K_M | 1.8 GB | 335.1 t/s | 262K | RUNS GREAT | S100 |
Q4_K_M·102.4 t/s tok/s·RUNS GREAT | Q4_K_M | 5.8 GB | 102.4 t/s | — | RUNS GREAT | S92 |
Q4_K_M·99.8 t/s tok/s·262K ctx·RUNS GREAT | Q4_K_M | 5.9 GB | 99.8 t/s | 262K | RUNS GREAT | S91 |
Q4_K_M·272.0 t/s tok/s·131K ctx·RUNS GREAT | Q4_K_M | 2.2 GB | 272.0 t/s | 131K | RUNS GREAT | S100 |
Q4_K_M·471.4 t/s tok/s·8K ctx·RUNS GREAT | Q4_K_M | 2.5 GB | 471.4 t/s | 8K | RUNS GREAT | S100 |
Q4_K_M·103.1 t/s tok/s·66K ctx·RUNS GREAT | Q4_K_M | 5.8 GB | 103.1 t/s | 66K | RUNS GREAT | S92 |
IQ3_XXS·51.5 t/s tok/s·262K ctx·DECENT | IQ3_XXS | 11.5 GB | 51.5 t/s | 262K | DECENT | B55 |
BF16·107.8 t/s tok/s·500K ctx·RUNS GREAT | BF16 | 5.5 GB | 107.8 t/s | 500K | RUNS GREAT | S93 |
Q4_K_M·114.7 t/s tok/s·33K ctx·RUNS GREAT | Q4_K_M | 5.2 GB | 114.7 t/s | 33K | RUNS GREAT | S94 |
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
Prompt Processing
Estimated for Nemotron Labs Diffusion 14B, the compute-bound phase that reads your prompt before the first reply token appears.
Short chat
451 ms
512 tok prompt
Long chat
3.6 s
4,096 tok prompt
Document / codebase
28.8 s
32,768 tok prompt
Prefill is compute-bound and a different number from the decode tok/s shown elsewhere on this page — how prompt processing works →
Efficiency & Value
Based on Nemotron Labs Diffusion 14B at ~67.2 tok/s decode.
Tokens per watt
Higher is better.
$ per tok/s (MSRP)
MSRP-based, not street price. Lower is better.
Performance figures are estimates calibrated as of 2026-09-21 — see calibration basis →
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 Qwen3.5 9B?
Yes, the NVIDIA GeForce RTX 3080 Ti with 12 GB can run Qwen3.5 9B, Mellum2 12B A2.5B Instruct, Gemma 4 12B IT, and 1925 other models. 1686 models run at excellent quality, and 141 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 1827 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
~93 tok/s~242 tok/s~72 tok/s~402 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 Qwen3.5 9B, Mellum2 12B A2.5B Instruct, Gemma 4 12B 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.
- 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.