Which AI models run on a NVIDIA RTX 4060 Ti 16 GB?
With 16 GB of VRAM, here are the popular models you can run locally (4,096-token context, ~32.0 GB system RAM assumed), ranked by popularity.
The NVIDIA RTX 4060 Ti 16 GB comes with 16 GB of VRAM. Among the popular GGUF models we track, it can run 33 of them entirely in VRAM — including Llama-3.2-1B-Instruct-Q8_0-GGUF, Qwen3-4B-GGUF, gpt-oss-20b-GGUF.
Larger models such as Qwen3-Coder-Next-GGUF still run on a NVIDIA RTX 4060 Ti 16 GB but require offloading part of the model to system RAM, which lowers speed. Models that exceed both VRAM and RAM are not listed.
| Model | Size | Quant. | Quality | Memory | Speed~ | Verdict |
|---|---|---|---|---|---|---|
| hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF | 1.24B | Q8_0 | Excellent | 2.57 GB | 325.1 t/s | Fits in VRAM |
| Qwen/Qwen3-4B-GGUF | 4.02B | Q8_0 | Excellent | 5.75 GB | 100.3 t/s | Fits in VRAM |
| unsloth/gpt-oss-20b-GGUF | 20.91B | F16 | Very good | 13.83 GB | 31.1 t/s | Fits in VRAM |
| janhq/Jan-v3.5-4B-gguf | 4.41B | GGUF | Excellent | 10.04 GB | 48.6 t/s | Fits in VRAM |
| bartowski/gemma-2-2b-it-GGUF | 2.61B | F32 | Excellent | 11.33 GB | 41.0 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-0.6B-GGUF | 0.75B | GGUF | Excellent | 2.62 GB | 284.6 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-14B-GGUF | 14.77B | Q6_K | Excellent | 13.94 GB | 35.4 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-8B-GGUF | 8.19B | Q6_K | Excellent | 8.44 GB | 63.9 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-32B-GGUF | 32.76B | Q2_K | Low | 15.06 GB | 34.8 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-1.7B-GGUF | 2.03B | GGUF | Excellent | 5.28 GB | 105.5 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-30B-A3B-GGUF | 30.53B | Q2_K | Low | 13.95 GB | 38.1 t/s | Fits in VRAM |
| bartowski/Meta-Llama-3.1-8B-Instruct-GGUF | 8.03B | Q8_0 | Excellent | 10.12 GB | 50.3 t/s | Fits in VRAM |
| Qwen/Qwen2.5-1.5B-Instruct-GGUF | 1.78B | GGUF | Excellent | 4.76 GB | 120.6 t/s | Fits in VRAM |
| unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF | 30.53B | Q3_K_S | Fair | 15.85 GB | 32.3 t/s | Fits in VRAM |
| MaziyarPanahi/Phi-3.5-mini-instruct-GGUF | 3.82B | Q8_0 | Excellent | 5.53 GB | 105.8 t/s | Fits in VRAM |
| Qwen/Qwen2.5-3B-Instruct-GGUF | 3.4B | GGUF | Excellent | 8.02 GB | 63.2 t/s | Fits in VRAM |
| bartowski/Llama-3.2-3B-Instruct-GGUF | 3.21B | F16 | Excellent | 7.66 GB | 66.8 t/s | Fits in VRAM |
| Qwen/Qwen2.5-0.5B-Instruct-GGUF | 0.63B | GGUF | Excellent | 2.36 GB | 339.1 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-4B-Instruct-2507-GGUF | 4.02B | GGUF | Excellent | 9.27 GB | 53.3 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-8B-A1B-GGUF | 8.47B | Q8_0 | Excellent | 10.6 GB | 47.7 t/s | Fits in VRAM |
| MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF | 7.25B | GGUF | Excellent | 15.6 GB | 29.6 t/s | Fits in VRAM |
| MaziyarPanahi/gemma-3-4b-it-GGUF | 3.88B | GGUF | Excellent | 8.98 GB | 55.3 t/s | Fits in VRAM |
| MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF | 8.03B | Q8_0 | Excellent | 10.12 GB | 50.3 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen2.5-7B-Instruct-GGUF | 7.62B | Q8_0 | Excellent | 9.67 GB | 53.0 t/s | Fits in VRAM |
| MaziyarPanahi/Phi-4-mini-instruct-GGUF | 3.84B | GGUF | Excellent | 8.9 GB | 55.9 t/s | Fits in VRAM |
| MaziyarPanahi/Yi-Coder-1.5B-Chat-GGUF | 1.48B | GGUF | Excellent | 4.14 GB | 145.4 t/s | Fits in VRAM |
| MaziyarPanahi/DeepSeek-R1-0528-Qwen3-8B-GGUF | 8.19B | Q6_K | Excellent | 8.44 GB | 63.9 t/s | Fits in VRAM |
| MaziyarPanahi/Mistral-Nemo-Instruct-2407-GGUF | 12.25B | Q8_0 | Excellent | 14.62 GB | 33.0 t/s | Fits in VRAM |
| MaziyarPanahi/Llama-3-8B-Instruct-32k-v0.1-GGUF | 8.03B | Q8_0 | Excellent | 10.12 GB | 50.3 t/s | Fits in VRAM |
| MaziyarPanahi/gemma-3-1b-it-GGUF | 1.0B | GGUF | Excellent | 3.15 GB | 214.0 t/s | Fits in VRAM |
| TheBloke/Mistral-7B-Instruct-v0.2-GGUF | 7.24B | Q8_0 | Excellent | 9.27 GB | 55.8 t/s | Fits in VRAM |
| MaziyarPanahi/Yi-Coder-9B-Chat-GGUF | 8.83B | Q5_K_M | Very good | 8.06 GB | 68.6 t/s | Fits in VRAM |
| MaziyarPanahi/gemma-3-12b-it-GGUF | 11.77B | Q8_0 | Excellent | 14.11 GB | 34.3 t/s | Fits in VRAM |
| unsloth/Qwen3-Coder-Next-GGUF | 79.67B | Q4_0 | Good | 47.32 GB | 1.2 t/s | Offload |
| Qwen/Qwen2.5-Coder-32B-Instruct-GGUF | 32.76B | Q5_K_M | Excellent | 46.89 GB | 1.2 t/s | Offload |
| MaziyarPanahi/Mixtral-8x22B-v0.1-GGUF | 140.62B | IQ1_M | Very low | 32.16 GB | 1.6 t/s | Offload |
| MaziyarPanahi/Llama-3.3-70B-Instruct-GGUF | 70.55B | Q4_K_M | Good | 44.45 GB | 1.3 t/s | Offload |
| MaziyarPanahi/Meta-Llama-3.1-70B-Instruct-GGUF | 70.55B | Q4_K_M | Good | 44.45 GB | 1.3 t/s | Offload |
"Fits in VRAM" = fast, fully on GPU. "Offload" = part on system RAM, slower. Speed is a rough estimate.
Frequently asked questions
How much VRAM does the NVIDIA RTX 4060 Ti 16 GB have?
The NVIDIA RTX 4060 Ti 16 GB has 16 GB of VRAM, which determines how large a model it can run entirely on the GPU.
What is the best LLM to run on a NVIDIA RTX 4060 Ti 16 GB?
Among popular models, hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF runs well on a NVIDIA RTX 4060 Ti 16 GB using the Q8_0 quantization (about 2.57 GB). Larger models trade speed for capability via RAM offloading.
Can a NVIDIA RTX 4060 Ti 16 GB run a 7–8B model?
Yes. A 7–8B model like Qwen3-8B-GGUF fits entirely in the 16 GB of a NVIDIA RTX 4060 Ti 16 GB (Q6_K).
Can a NVIDIA RTX 4060 Ti 16 GB run a 13–14B model?
Yes. A 13–14B model like Qwen3-14B-GGUF fits entirely in the 16 GB of a NVIDIA RTX 4060 Ti 16 GB (Q6_K).
Can a NVIDIA RTX 4060 Ti 16 GB run a 70B model?
Only with offloading. A 70B model like Qwen3-Coder-Next-GGUF runs on a NVIDIA RTX 4060 Ti 16 GB by using system RAM in addition to its 16 GB, which is slower.