Best local AI models for NVIDIA RTX 2080 Laptop
8 GB GDDR6. At a 4k context, 123 of the 233 models in our catalog with verified parameter counts fit fully, up to Mochi 1 at 10B parameters.
Check your own machine against every model →The largest models that fit fully
The 30 largest of the 123 models that fit; every smaller model in the catalog fits too. Best quant means the highest quality compression whose weights and 4k context both sit inside the memory.
| Model | Parameters | Best quant that fits | Memory used at 4k |
|---|---|---|---|
| Mochi 1 | 10B | Q4_K_M | 7.3 GB |
| Gemma 2 9B | 9B | Q4_K_M | 8 GB |
| Nemotron Nano 4B / 9B | 9B | Q5_K_M | 7.7 GB |
| GLM-4 9B / GLM-4.5-Air | 9B | Q5_K_M | 7.7 GB |
| Yi-Coder 1.5B / 9B | 9B | Q5_K_M | 7.7 GB |
| GLM-4-9B-Chat / CodeGeeX4 | 9B | Q5_K_M | 7.7 GB |
| GLM-4V-9B / GLM-4.1V-Thinking | 9B | Q5_K_M | 7.7 GB |
| Chroma | 8.9B | Q5_K_M | 7.6 GB |
| Llama 3.1 8B | 8B | Q5_K_M | 7.4 GB |
| Granite 3.3 2B / 8B | 8B | Q6_K | 7.9 GB |
| Ministral 3B / 8B | 8B | Q6_K | 7.9 GB |
| InternLM 3 8B | 8B | Q6_K | 7.9 GB |
| OpenCoder 1.5B / 8B | 8B | Q6_K | 7.9 GB |
| Seed-Coder 8B | 8B | Q6_K | 7.9 GB |
| MiniCPM-V 2.6 / MiniCPM-o 2.6 | 8B | Q6_K | 7.9 GB |
| Idefics 3 8B | 8B | Q6_K | 7.9 GB |
| Fuyu-8B | 8B | Q6_K | 7.9 GB |
| Emu3 | 8B | Q6_K | 7.9 GB |
| Stable Diffusion 3.5 Large / Turbo | 8B | Q6_K | 7.9 GB |
| EXAONE 3.5 2.4B / 7.8B | 7.8B | Q6_K | 7.7 GB |
| Mistral 7B | 7B | Q6_K | 7.4 GB |
| Qwen2.5 0.5B / 1.5B / 3B / 7B | 7B | Q6_K | 6.9 GB |
| OLMo 2 1B / 7B | 7B | Q6_K | 6.9 GB |
| Falcon 3 1B / 3B / 7B | 7B | Q6_K | 6.9 GB |
| Command R7B | 7B | Q6_K | 6.9 GB |
| OpenHermes 2.5 | 7B | Q6_K | 6.9 GB |
| Zephyr 7B Beta | 7B | Q6_K | 6.9 GB |
| OpenChat 3.5 | 7B | Q6_K | 6.9 GB |
| Starling LM 7B | 7B | Q6_K | 6.9 GB |
| Codestral Mamba 7B | 7B | Q6_K | 6.9 GB |
Close, but only with CPU offload
These need more than the card holds at their smallest practical quant, so part of the model runs from system memory (figures assume 32 GB of it). They work, several times slower.
| Model | Parameters | Memory at Q4_K_M | System RAM at 4k |
|---|---|---|---|
| Open-Sora 2.0 | 11B | 8.1 GB needed | 10.1 GB |
| FLUX.1 dev | 12B | 14.4 GB needed | 16.4 GB |
| Gemma 3 12B | 12B | 8.8 GB needed | 10.8 GB |
| Gemma 4 12B | 12B | 8.8 GB needed | 10.8 GB |
| Mistral NeMo 12B | 12B | 8.8 GB needed | 10.8 GB |
| Pixtral 12B | 12B | 8.8 GB needed | 10.8 GB |
| FLUX.1 schnell | 12B | 8.8 GB needed | 10.8 GB |
| FLUX.1 Kontext dev | 12B | 8.8 GB needed | 10.8 GB |
| FLUX.1 Krea dev | 12B | 8.8 GB needed | 10.8 GB |
| Vicuna 13B | 13B | 9.5 GB needed | 11.5 GB |
How to read this
The NVIDIA RTX 2080 Laptop graphics card features 8 GB of GDDR6 dedicated video memory. This memory size is the absolute limit for running local AI models entirely on your GPU. To run a model smoothly, the model files and the active workspace must fit inside this 8 GB boundary. If a model exceeds this limit, your system will slow down significantly or fail to run the model.
The quant column shows the quantization level used to compress these models. Quantization reduces the precision of model weights to save memory. For example, the Q4_K_M quant allows the 10B Mochi 1 model to fit into 7.3 GB of VRAM. The Q6_K quant offers higher precision for smaller models. It allows the 8B Llama 3.1 to run at 7.4 GB used, and the 7B Mistral to run at 7.4 GB used.
When a model is slightly too large for the 8 GB VRAM, you can use CPU offload. This process shares the workload between your GPU and your system RAM. Assuming your laptop has 32 GB of system RAM, you can run larger models like the 12B Gemma 3 or the 12B Mistral NeMo. These models need 8.8 GB at Q4_K_M, which requires 10.8 GB of system RAM to offload the extra data.
CPU offload comes with a performance cost. Moving data between system RAM and GPU video memory is much slower than keeping everything on the GPU. Models like the 12B FLUX.1 schnell require 8.8 GB at Q4_K_M and 10.8 GB of system RAM. The 13B Vicuna requires 9.5 GB at Q4_K_M and 11.5 GB of system RAM. These models will run at a lower speed compared to fully on-card models.
You must also consider the context window when planning your memory usage. The memory figures listed here are calculated using a basic 4k context window. If you increase the context length to process longer documents or chat histories, the model will require more memory. A model like Gemma 2 9B at Q4_K_M uses exactly 8 GB, so expanding its context window beyond 4k will exceed your VRAM limit.