Best local AI models for NVIDIA RTX A4000

The largest models that fit fully

The 30 largest of the 155 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 A4000 graphics card features 16 GB of GDDR6 memory. This dedicated memory determines the maximum size of the artificial intelligence models you can run locally. To load a model entirely on your graphics hardware, the model files and the active context data must fit within this 16 GB limit.

The quantization column indicates the compression level used to shrink these models. Quantization reduces the precision of model weights to save space. For example, the 21B gpt-oss-20b and Reka Flash 3 models fit in 15.4 GB of memory when compressed to the Q4_K_M quantization. Smaller models like the 12B Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, and Pixtral 12B can run at a higher Q8_0 quantization while using 15.3 GB of memory.

When a model exceeds the 16 GB limit of your graphics card, you can offload parts of the workload to your system RAM. This process requires a system with at least 32 GB of system RAM. Offloading allows you to run larger models, but it reduces processing speed because transferring data between system RAM and graphics memory is slow.

Examples of offloading include running the 22B Solar Pro or Codestral 22B at Q4_K_M quantization. These models need 16.1 GB of memory and require 18.1 GB of system RAM. The 24B Mistral Small 3.2, Magistral Small, and Devstral Small 1.1 models require 17.6 GB of memory and 19.6 GB of system RAM. Larger options like the Gemma 3 27B require 19.8 GB of memory and 21.8 GB of system RAM.

You must also reserve memory for the context window. The memory figures listed for these models assume a basic 4k context window. If you increase the context window to process longer documents or chat histories, the model will require significantly more memory. This extra demand may force you to use a lower quantization or offload more data to your system RAM.