Which AI models run on a NVIDIA RTX 3080 Ti Laptop?
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.
See also: Best GPU for running local LLMs.
The NVIDIA RTX 3080 Ti Laptop comes with 16 GB of VRAM. Among the popular GGUF models we track, it can run 33 of them entirely in VRAM — including Qwen3-Coder-30B-A3B-Instruct-GGUF, Qwen-AgentWorld-35B-A3B-GGUF, gpt-oss-20b-GGUF.
With 16 GB you can typically run a 14B at high quality, or a 24–27B at 4-bit. Which quantization is best depends on the exact model and your context length. For a full shortlist, see the best LLM for 16 GB of VRAM.
Larger models such as Qwen2.5-Coder-32B-Instruct-GGUF still run on a NVIDIA RTX 3080 Ti Laptop but require offloading part of the model to system RAM, which lowers speed. Models that exceed both VRAM and RAM are not listed.
33 fit fully in VRAM · 4 run with offload
| Model | Size | Quant. | Quality | Memory | Speed~ | Verdict |
|---|---|---|---|---|---|---|
| unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF | 30.53B | Q3_K_M | Fair |
14.88 GB
|
29.2 t/s | Fits in VRAM |
| unsloth/Qwen-AgentWorld-35B-A3B-GGUF | 34.66B | IQ3_S | Fair |
14.83 GB
|
28.7 t/s | Fits in VRAM |
| unsloth/gpt-oss-20b-GGUF | 20.91B | F16 | Very good |
13.74 GB
|
31.1 t/s | Fits in VRAM |
| janhq/Jan-v3.5-4B-gguf | 4.41B | GGUF | Excellent |
9.59 GB
|
48.6 t/s | Fits in VRAM |
| unsloth/Qwen3-8B-GGUF | 8.19B | Q8_K_XL | Excellent |
11.44 GB
|
39.7 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 | Q8_0 | Excellent |
9.23 GB
|
50.3 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 | Q6_K | Excellent |
12.67 GB
|
34.3 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 | Q6_K | Excellent |
12.71 GB
|
35.4 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 |
| MaziyarPanahi/Qwen3-32B-GGUF | 32.76B | Q2_K | Low |
13.3 GB
|
34.8 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-30B-A3B-GGUF | 30.53B | Q3_K_L | Fair |
15.98 GB
|
27.0 t/s | Fits in VRAM |
| unsloth/Ornith-1.0-35B-GGUF | 34.66B | IQ3_S | Fair |
14.83 GB
|
28.7 t/s | Fits in VRAM |
| bartowski/Qwen2.5-7B-Instruct-GGUF | 7.62B | F16 | Excellent |
15.21 GB
|
28.2 t/s | Fits in VRAM |
| bartowski/gemma-2-2b-it-GGUF | 2.61B | F32 | Excellent |
10.82 GB
|
41.0 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 | GGUF | Excellent |
8.86 GB
|
53.3 t/s | Fits in VRAM |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-GGUF | 8.19B | Q8_0 | Excellent |
9.47 GB
|
49.3 t/s | Fits in VRAM |
| google/gemma-2b | 2.51B | GGUF | Excellent |
10.41 GB
|
42.8 t/s | Fits in VRAM |
| bartowski/Qwen2.5-14B-Instruct-GGUF | 14.77B | Q6_K_L | Excellent |
13.19 GB
|
34.4 t/s | Fits in VRAM |
| bartowski/Qwen2.5-32B-Instruct-GGUF | 32.76B | IQ3_M | Fair |
15.59 GB
|
29.0 t/s | Fits in VRAM |
| bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF | 34.66B | Q3_K_M | Fair |
15.99 GB
|
26.5 t/s | Fits in VRAM |
| MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF | 8.03B | Q8_0 | Excellent |
9.23 GB
|
50.3 t/s | Fits in VRAM |
| MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF | 7.25B | GGUF | Excellent |
14.8 GB
|
29.6 t/s | Fits in VRAM |
| MaziyarPanahi/gemma-3-4b-it-GGUF | 3.88B | GGUF | Excellent |
8.37 GB
|
55.3 t/s | Fits in VRAM |
| Qwen/Qwen2.5-Coder-32B-Instruct-GGUF | 32.76B | Q5_K_M | Excellent |
45.13 GB
|
1.2 t/s | Offload |
| unsloth/Qwen3-Coder-Next-GGUF | 79.67B | Q4_1 | Very good |
47.79 GB
|
1.1 t/s | Offload |
| unsloth/Laguna-S-2.1-GGUF | 117.56B | IQ3_S | Low |
46.09 GB
|
1.1 t/s | Offload |
| MaziyarPanahi/Mixtral-8x22B-v0.1-GGUF | 140.62B | IQ1_M | Very low |
32.16 GB
|
1.6 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 3080 Ti Laptop have?
The NVIDIA RTX 3080 Ti Laptop 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 3080 Ti Laptop?
Among popular models, unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF runs well on a NVIDIA RTX 3080 Ti Laptop using the Q3_K_M quantization (about 14.88 GB). With 16 GB you can generally run a 14B at high quality, or a 24–27B at 4-bit. Larger models trade speed for capability via RAM offloading. See the best LLM for 16 GB of VRAM.
Can a NVIDIA RTX 3080 Ti Laptop run a 7–8B model?
Yes. A 7–8B model like Qwen3-8B-GGUF fits entirely in the 16 GB of a NVIDIA RTX 3080 Ti Laptop (Q8_K_XL).
Can a NVIDIA RTX 3080 Ti Laptop run a 13–14B model?
Yes. A 13–14B model like Qwen3-14B-GGUF fits entirely in the 16 GB of a NVIDIA RTX 3080 Ti Laptop (Q6_K).
Can a NVIDIA RTX 3080 Ti Laptop run a 70B model?
Only with offloading. A 70B model like Qwen3-Coder-Next-GGUF runs on a NVIDIA RTX 3080 Ti Laptop by using system RAM in addition to its 16 GB, which is slower.