Best local AI models for NVIDIA RTX A400

The largest models that fit fully

The 30 largest of the 81 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 A400 is an entry level professional graphics card equipped with 4 GB of GDDR6 memory. This dedicated onboard memory determines the maximum size of the artificial intelligence models you can run entirely on the hardware. To execute local models successfully, the model weight files must fit within this 4 GB limit while leaving a small amount of headroom for processing tasks.

The quant column indicates the quantization level used to compress the model weights. Quantization reduces the precision of the numerical values to save space. For example, a Q4_K_M quant uses a four bit format to fit larger models like Lumina-Next or CogVideoX 2B / 5B into 3.7 GB of memory. Smaller models like SmolLM3 3B can run at a higher Q8_0 quant which uses eight bits and requires 3.8 GB of memory for better output quality.

Several highly capable models fit directly into the local memory of the card. You can run Phi-3.5-vision at a Q5_K_M quant using 3.6 GB of memory. Text generation models like Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, and Danube 3 4B fit comfortably at a Q6_K quant using 3.9 GB of memory. For audio tasks, Fish Speech 1.5 / OpenAudio S1 uses 3.9 GB at a Q6_K quant, while Higgs Audio v2 uses 3.8 GB at a Q8_0 quant.

If a model exceeds the 4 GB limit, you must use CPU offload. This technique splits the model layers between the graphics card and your system RAM. Running Mistral 7B at a Q4_K_M quant requires 5.7 GB of total memory, which uses your graphics card and 7.7 GB of system RAM. Similarly, Qwen2.5 0.5B / 1.5B / 3B / 7B at a Q4_K_M quant requires 5.1 GB of memory and 7.1 GB of system RAM.

CPU offload allows you to run larger models like Falcon 3 1B / 3B / 7B, Command R7B, and OpenHermes 2.5, but it comes with a performance cost. Transferring data between the system RAM and the graphics card over the system bus is much slower than using the dedicated GDDR6 memory. This transfer bottleneck significantly reduces the generation speed of your local setup.

You must also consider the memory cost of the context window. The memory figures listed are for the base models only. Running a model with a standard 4k context window requires additional memory to store the active conversation history. When operating close to the 4 GB limit of the hardware, a large context window can easily exceed the available memory and trigger slow system RAM offloading.