Best local AI models for NVIDIA RTX 3060 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 3060 8GB graphics card features 8 GB of GDDR6 memory. This dedicated video memory determines which local AI models you can run entirely on your hardware. To run a model smoothly without slowdowns, the model files and the active memory must fit within this 8 GB limit. If a model exceeds this capacity, your system must use slower memory alternatives.

Quantization is a method that compresses AI models to make them fit into smaller memory spaces. The quantization column shows the best available format for each model on this hardware. For example, Gemma 2 9B fits at the Q4_K_M quantization level using 8 GB of memory. Other models like Llama 3.1 8B run at the Q5_K_M quantization level using 7.4 GB of memory. Models like Mistral 7B can run at the higher quality Q6_K quantization level using 7.4 GB of memory.

When a model size exceeds your 8 GB of video memory, you must use CPU offload. This process shares the workload between your graphics card and your system RAM. You need a system with at least 32 GB of system RAM to use this method. For instance, Gemma 3 12B requires 8.8 GB of video memory at Q4_K_M quantization and 10.8 GB of system RAM. FLUX.1 dev requires 14.4 GB of video memory at FP8 or optimized settings and 16.4 GB of system RAM.

CPU offload allows you to run larger models like Vicuna 13B which needs 9.5 GB of video memory at Q4_K_M quantization and 11.5 GB of system RAM. However, transferring data between your graphics card and system RAM is slow. This transfer speed bottleneck will significantly reduce your generation speed. For the fastest performance, you should select models that fit completely within your 8 GB of video memory.

You must also consider the memory required for context length. The memory figures listed for these models assume a standard 4k context window. If you increase the context window to process longer documents or chat histories, the model will require more memory. Running a model very close to your 8 GB limit might cause slowdowns if your context window grows too large during a session.