Which AI models run on a NVIDIA RTX 4060 Laptop?
With 8 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 4060 Laptop comes with 8 GB of VRAM. Among the popular GGUF models we track, it can run 24 of them entirely in VRAM — including Jan-v3.5-4B-gguf, Qwen3-8B-GGUF, Llama-3.2-1B-Instruct-Q8_0-GGUF.
With 8 GB you can typically run a 7–8B model at Q6, entirely in VRAM. Which quantization is best depends on the exact model and your context length. For a full shortlist, see the best LLM for 8 GB of VRAM.
Larger models such as Qwen3-Coder-30B-A3B-Instruct-GGUF still run on a NVIDIA RTX 4060 Laptop but require offloading part of the model to system RAM, which lowers speed. Models that exceed both VRAM and RAM are not listed.
24 fit fully in VRAM · 10 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 | Q6_K | Excellent |
7.63 GB
|
63.9 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 | Q6_K_L | Excellent |
7.66 GB
|
62.7 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 | GGUF | Excellent |
7.27 GB
|
63.2 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 | BF16 | Excellent |
7.45 GB
|
63.1 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/Qwen3-14B-GGUF | 14.77B | Q2_K | Low |
6.78 GB
|
74.6 t/s | Fits in VRAM |
| bartowski/Llama-3.2-3B-Instruct-GGUF | 3.21B | F16 | Excellent |
7.09 GB
|
66.8 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 | Q6_K_L | Excellent |
7.09 GB
|
65.9 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 | Q8_0 | Excellent |
6.08 GB
|
105.8 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 |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF | 8.19B | Q6_K | Excellent |
7.63 GB
|
63.9 t/s | Fits in VRAM |
| bartowski/Qwen2.5-14B-Instruct-GGUF | 14.77B | IQ3_M | Fair |
7.99 GB
|
62.1 t/s | Fits in VRAM |
| MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF | 8.03B | Q6_K | Excellent |
7.42 GB
|
65.1 t/s | Fits in VRAM |
| MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF | 7.25B | Q6_K | Excellent |
6.84 GB
|
72.2 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 | IQ2_XXS | Very low |
22.89 GB
|
2.3 t/s | Offload |
| MaziyarPanahi/Qwen3-32B-GGUF | 32.76B | Q5_K_M | Very good |
23.42 GB
|
2.3 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/Qwen2.5-32B-Instruct-GGUF | 32.76B | Q5_K_L | Very good |
23.91 GB
|
2.3 t/s | Offload |
| bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF | 34.66B | Q5_K_S | Very good |
23.38 GB
|
2.2 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 Laptop have?
The NVIDIA RTX 4060 Laptop has 8 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 Laptop?
Among popular models, janhq/Jan-v3.5-4B-gguf runs well on a NVIDIA RTX 4060 Laptop using the Q8_0 quantization (about 5.73 GB). With 8 GB you can generally run a 7–8B model at Q6, entirely in VRAM. Larger models trade speed for capability via RAM offloading. See the best LLM for 8 GB of VRAM.
Can a NVIDIA RTX 4060 Laptop run a 7–8B model?
Yes. A 7–8B model like Qwen3-8B-GGUF fits entirely in the 8 GB of a NVIDIA RTX 4060 Laptop (Q6_K).
Can a NVIDIA RTX 4060 Laptop run a 13–14B model?
Yes. A 13–14B model like Qwen3-14B-GGUF fits entirely in the 8 GB of a NVIDIA RTX 4060 Laptop (Q2_K).
Can a NVIDIA RTX 4060 Laptop run a 70B model?
Only with offloading. A 70B model like Qwen3-Coder-Next-GGUF runs on a NVIDIA RTX 4060 Laptop by using system RAM in addition to its 8 GB, which is slower.