Which AI models run on a NVIDIA RTX 3060 Laptop?
With 6 GB of VRAM, here are the popular models you 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 3060 Laptop comes with 6 GB of VRAM. Among the popular GGUF models we track, it can run 22 of them entirely in VRAM — including Jan-v3.5-4B-gguf, Qwen3-8B-GGUF, Llama-3.2-1B-Instruct-Q8_0-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 3060 Laptop but require offloading part of the model to system RAM, which lowers speed. Models that exceed both VRAM and RAM are not listed.
22 fit fully in VRAM · 12 run with offload
| Model | Size | Quant. | Quality | Memory | Speed~ | Verdict |
|---|---|---|---|---|---|---|
| janhq/Jan-v3.5-4B-gguf | 4.41B | Q8_0 | Excellent |
5.73 GB
|
91.5 t/s | Fits in VRAM |
| unsloth/Qwen3-8B-GGUF | 8.19B | Q4_K_S | Good |
5.83 GB
|
89.4 t/s | Fits in VRAM |
| hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF | 1.24B | Q8_0 | Excellent |
2.22 GB
|
325.1 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 |
| Qwen/Qwen3-4B-GGUF | 4.02B | Q8_0 | Excellent |
5.35 GB
|
100.3 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 | 1.78B | 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 |
| Qwen/Qwen2.5-3B-Instruct-GGUF | 3.4B | Q8_0 | Excellent |
4.31 GB
|
118.8 t/s | Fits in VRAM |
| Qwen/Qwen2.5-0.5B-Instruct-GGUF | 0.63B | GGUF | Excellent |
2.03 GB
|
339.1 t/s | Fits in VRAM |
| ibm-granite/granite-4.1-3b-GGUF | 3.4B | Q8_0 | Excellent |
4.48 GB
|
118.7 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-0.6B-GGUF | 0.75B | GGUF | Excellent |
2.64 GB
|
284.6 t/s | Fits in VRAM |
| bartowski/Llama-3.2-3B-Instruct-GGUF | 3.21B | Q8_0 | Excellent |
4.29 GB
|
125.5 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 |
| bartowski/gemma-2-2b-it-GGUF | 2.61B | Q8_0 | Excellent |
3.67 GB
|
154.2 t/s | Fits in VRAM |
| bartowski/Phi-3.5-mini-instruct-GGUF | 3.82B | Q6_K_L | Excellent |
5.26 GB
|
134.9 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 |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF | 8.19B | Q3_K_L | Good |
5.49 GB
|
96.9 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 |
| MaziyarPanahi/gemma-3-4b-it-GGUF | 3.88B | Q8_0 | Excellent |
4.98 GB
|
104.0 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/Qwen-AgentWorld-35B-A3B-GGUF | 34.66B | Q4_K_XL | Very good |
21.67 GB
|
2.4 t/s | Offload |
| unsloth/gpt-oss-20b-GGUF | 20.91B | F16 | Very good |
13.74 GB
|
3.9 t/s | Offload |
| unsloth/Qwen3-Coder-Next-GGUF | 79.67B | IQ1_M | Very low |
21.39 GB
|
2.5 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 | 34.66B | Q4_K_XL | Very good |
21.67 GB
|
2.4 t/s | Offload |
| google/gemma-2b | 2.51B | GGUF | Excellent |
10.41 GB
|
5.4 t/s | Offload |
| bartowski/Qwen2.5-14B-Instruct-GGUF | 14.77B | Q8_0 | Excellent |
16.17 GB
|
3.4 t/s | Offload |
| bartowski/Qwen2.5-32B-Instruct-GGUF | 32.76B | Q4_K_L | Good |
20.83 GB
|
2.6 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 |
"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 3060 Laptop have?
The NVIDIA RTX 3060 Laptop 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 3060 Laptop?
Among popular models, janhq/Jan-v3.5-4B-gguf runs well on a NVIDIA RTX 3060 Laptop using the Q8_0 quantization (about 5.73 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 3060 Laptop run a 7–8B model?
Yes. A 7–8B model like Qwen3-8B-GGUF fits entirely in the 6 GB of a NVIDIA RTX 3060 Laptop (Q4_K_S).
Can a NVIDIA RTX 3060 Laptop run a 13–14B model?
Only with offloading. A 13–14B model like Qwen3-14B-GGUF runs on a NVIDIA RTX 3060 Laptop by using system RAM in addition to its 6 GB, which is slower.
Can a NVIDIA RTX 3060 Laptop run a 70B model?
Only with offloading. A 70B model like Qwen3-Coder-Next-GGUF runs on a NVIDIA RTX 3060 Laptop by using system RAM in addition to its 6 GB, which is slower.