Which AI models run on a NVIDIA RTX 2060?
The NVIDIA RTX 2060 has 6 GB of VRAM. Here are the popular AI models it can run locally (4,096-token context, ~16.0 GB system RAM assumed), ranked by popularity.
See also: Best GPU for running local LLMs.
The NVIDIA RTX 2060 comes with 6 GB of VRAM. Among the popular GGUF models we track, it can run 23 of them entirely in VRAM — including Qwen3-4B-GGUF, Meta-Llama-3.1-8B-Instruct-GGUF, Jan-v3.5-4B-gguf.
With 6 GB you can typically run smaller models, typically up to about 3–4B. Which quantization is best depends on the exact model and your context length.
Larger models such as Qwen3-Coder-30B-A3B-Instruct-GGUF still run on a NVIDIA RTX 2060 but require offloading part of the model to system RAM, which lowers speed. Models that exceed both VRAM and RAM are not listed.
23 fit fully in VRAM · 12 run with offload
| Model | Size | Quant. | Quality | Memory | Speed~ | Verdict |
|---|---|---|---|---|---|---|
| MaziyarPanahi/Qwen3-4B-GGUF | 4.02B | Q6_K | Excellent |
4.44 GB
|
129.9 t/s | Fits in VRAM |
| bartowski/Meta-Llama-3.1-8B-Instruct-GGUF | 8.03B | Q4_K_M | Good |
5.86 GB
|
87.3 t/s | Fits in VRAM |
| janhq/Jan-v3.5-4B-gguf | 4.41B | Q8_0 | Excellent |
5.73 GB
|
91.5 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-0.6B-GGUF | 0.75B | GGUF | Excellent |
2.64 GB
|
284.6 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-1.7B-GGUF | 2.03B | GGUF | Excellent |
5.03 GB
|
105.5 t/s | Fits in VRAM |
| bartowski/Qwen2.5-7B-Instruct-GGUF | 7.62B | Q5_K_S | Very good |
5.97 GB
|
80.8 t/s | Fits in VRAM |
| Qwen/Qwen2.5-3B-Instruct-GGUF | 3.09B | Q8_0 | Excellent |
4.31 GB
|
118.8 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-2.6B-GGUF | 2.7B | BF16 | Excellent |
5.89 GB
|
79.5 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-1.2B-Instruct-GGUF | 1.17B | BF16 | Excellent |
3.03 GB
|
183.3 t/s | Fits in VRAM |
| Qwen/Qwen2.5-1.5B-Instruct-GGUF | 1.54B | GGUF | Excellent |
4.23 GB
|
120.6 t/s | Fits in VRAM |
| Qwen/Qwen2.5-Coder-7B-Instruct-GGUF | 7.62B | Q5_0 | Very good |
5.97 GB
|
80.8 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-4B-Instruct-2507-GGUF | 4.02B | Q6_K | Excellent |
4.44 GB
|
129.9 t/s | Fits in VRAM |
| MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF | 8.03B | Q4_K_M | Good |
5.86 GB
|
87.3 t/s | Fits in VRAM |
| MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF | 7.25B | Q5_K_S | Very good |
5.96 GB
|
85.9 t/s | Fits in VRAM |
| Qwen/Qwen2.5-0.5B-Instruct-GGUF | 0.49B | GGUF | Excellent |
2.03 GB
|
339.1 t/s | Fits in VRAM |
| MaziyarPanahi/gemma-3-4b-it-GGUF | 4.3B | Q8_0 | Excellent |
5.0 GB
|
104.0 t/s | Fits in VRAM |
| MaziyarPanahi/Phi-3.5-mini-instruct-GGUF | 3.82B | Q6_K | Excellent |
5.22 GB
|
137.0 t/s | Fits in VRAM |
| lmstudio-community/Llama-3.2-3B-Instruct-GGUF | 3.21B | Q8_0 | Excellent |
4.29 GB
|
125.5 t/s | Fits in VRAM |
| MaziyarPanahi/Llama-3-8B-Instruct-32k-v0.1-GGUF | 8.03B | Q4_K_M | Good |
5.86 GB
|
87.3 t/s | Fits in VRAM |
| MaziyarPanahi/Yi-1.5-6B-Chat-GGUF | 6.06B | Q6_K | Excellent |
5.68 GB
|
86.3 t/s | Fits in VRAM |
| MaziyarPanahi/Llama-3.2-1B-Instruct-GGUF | 1.24B | GGUF | Excellent |
3.3 GB
|
173.2 t/s | Fits in VRAM |
| MaziyarPanahi/Mistral-Nemo-Instruct-2407-GGUF | 12.25B | Q2_K | Low |
5.89 GB
|
89.6 t/s | Fits in VRAM |
| MaziyarPanahi/WizardLM-2-7B-GGUF | 7.24B | Q5_K_S | Very good |
5.91 GB
|
85.9 t/s | Fits in VRAM |
| unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF | 30.53B | Q5_K_XL | Very good |
21.42 GB
|
2.5 t/s | Offload |
| unsloth/gpt-oss-20b-GGUF | 20.91B | F16 | Very good |
13.74 GB
|
3.9 t/s | Offload |
| unsloth/Qwen-AgentWorld-35B-A3B-GGUF | 34.66B | Q4_K_XL | Very good |
21.67 GB
|
2.4 t/s | Offload |
| Qwen/Qwen3-8B-GGUF | 8.19B | Q8_0 | Excellent |
9.47 GB
|
6.2 t/s | Offload |
| MaziyarPanahi/Qwen3-14B-GGUF | 14.77B | Q6_K | Excellent |
12.71 GB
|
4.4 t/s | Offload |
| MaziyarPanahi/Qwen3-32B-GGUF | 32.76B | Q4_K_M | Good |
20.2 GB
|
2.7 t/s | Offload |
| MaziyarPanahi/Qwen3-30B-A3B-GGUF | 30.53B | Q5_K_M | Very good |
21.41 GB
|
2.5 t/s | Offload |
| unsloth/Ornith-1.0-35B-GGUF | — | Q4_K_XL | Excellent |
21.67 GB
|
2.4 t/s | Offload |
| bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF | 34.66B | Q4_1 | Very good |
21.34 GB
|
2.4 t/s | Offload |
| unsloth/Qwen3-Coder-Next-GGUF | 79.67B | IQ1_M | Very low |
21.39 GB
|
2.5 t/s | Offload |
| MaziyarPanahi/Mistral-Small-24B-Instruct-2501-GGUF | 23.57B | Q6_K | Excellent |
19.44 GB
|
2.8 t/s | Offload |
| unsloth/GLM-4.7-Flash-GGUF | 31.22B | Q5_K_XL | Very good |
21.21 GB
|
2.5 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 2060 have?
The NVIDIA RTX 2060 has 6 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 2060?
Among popular models, MaziyarPanahi/Qwen3-4B-GGUF runs well on a NVIDIA RTX 2060 using the Q6_K quantization (about 4.44 GB). With 6 GB you can generally run smaller models, typically up to about 3–4B. Larger models trade speed for capability via RAM offloading.
Can a NVIDIA RTX 2060 run a 7–8B model?
Yes. A 7–8B model like Meta-Llama-3.1-8B-Instruct-GGUF fits entirely in the 6 GB of a NVIDIA RTX 2060 (Q4_K_M).
Can a NVIDIA RTX 2060 run a 13–14B model?
Yes. A 13–14B model like Mistral-Nemo-Instruct-2407-GGUF fits entirely in the 6 GB of a NVIDIA RTX 2060 (Q2_K).
Can a NVIDIA RTX 2060 run a 70B model?
Only with offloading. A 70B model like Qwen3-Coder-Next-GGUF runs on a NVIDIA RTX 2060 by using system RAM in addition to its 6 GB, which is slower.