Best local AI models for NVIDIA RTX 3050 8GB

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.

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.

How to read this

The NVIDIA RTX 3050 graphics card features 8 GB GDDR6 memory. This onboard memory determines the size of the artificial intelligence models you can run locally. To run a model completely on your graphics card, the model files and the active memory must fit entirely within this 8 GB limit. Running models locally on your hardware ensures complete privacy and removes any need for an internet connection.

Quantization is a method that compresses model files to save space. The quant column shows the best format to balance performance and memory usage. For example, Gemma 2 9B fits in your memory at the Q4_K_M quantization level which uses 8 GB of memory. Other models like Llama 3.1 8B fit at the Q5_K_M quantization level using 7.4 GB of memory. Many models like Mistral 7B and Qwen2.5 7B can run at the higher quality Q6_K quantization level using 7.4 GB and 6.9 GB of memory respectively.

If a model is too large for your 8 GB graphics card, you can use CPU offload. This process splits the model between your graphics card memory and your system memory. We assume your computer has 32 GB system RAM for these cases. For instance, FLUX.1 dev needs 14.4 GB at FP8 or optimized settings which requires 16.4 GB of system RAM. Gemma 3 12B and Mistral NeMo 12B both need 8.8 GB at the Q4_K_M quantization level which requires 10.8 GB of system RAM.

CPU offload allows you to run larger models like Vicuna 13B which needs 9.5 GB at Q4_K_M and 11.5 GB of system RAM. However, offloading comes with a performance cost. Moving data between your system RAM and your graphics card is much slower than keeping everything on the graphics card. Your generation speed will drop significantly when you offload parts of the model to your system memory.

You must also consider the context window size when planning your memory usage. The memory figures listed are calculated using a standard 4k context window. If you increase the context window to process longer documents or longer chat histories, the model will require more memory. This extra memory demand might force you to use a lower quantization level or switch to CPU offload to avoid running out of memory.