Best local AI models for NVIDIA RTX 3080 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 3080 Laptop graphics card features 8 GB of GDDR6 memory. This dedicated video memory determines which local artificial intelligence models can run entirely on your hardware. For the best performance, a model and its working memory must fit completely within this 8 GB limit. If a model exceeds this capacity, your system must use slower memory alternatives.
The quantization column indicates the compression level applied to each model. Quantization reduces the size of model weights to save memory. A Q4_K_M quant represents a four bit quantization level that balances size and quality. A Q5_K_M or Q6_K quant offers higher precision but requires more memory. For example, Gemma 2 9B fits at Q4_K_M using 8 GB of memory, while Llama 3.1 8B fits at Q5_K_M using 7.4 GB of memory.
Smaller models can run at higher precision levels on this hardware. Granite 3.3 8B, Ministral 8B, and InternLM 3 8B all run at Q6_K precision using 7.9 GB of memory. You can also run Mistral 7B at Q6_K using 7.4 GB of memory. Popular seven billion parameter models like Qwen2.5 7B, Falcon 3 7B, and Command R7B run at Q6_K precision using 6.9 GB of memory.
When a model exceeds the 8 GB video memory limit, you must use CPU offload. This process shares the workload between your graphics card and your system RAM. CPU offload allows you to run larger models but reduces processing speed. For this setup, we assume your laptop has 32 GB of system RAM to handle the overflow.
Several models require CPU offload on this hardware configuration. Mistral NeMo 12B, Pixtral 12B, and FLUX.1 schnell require 8.8 GB of memory at Q4_K_M, which uses 10.8 GB of system RAM. FLUX.1 dev requires 14.4 GB of memory at FP8 and uses 16.4 GB of system RAM. Vicuna 13B requires 9.5 GB of memory at Q4_K_M and uses 11.5 GB of system RAM.
Memory calculations for these models are based on a standard 4k context window. As your conversation history grows, the model requires additional memory to track the context. Running models close to the 8 GB limit may cause slowdowns if your chat history exceeds this limit. You can lower the quantization level to free up memory for longer conversations.