Best local AI models for NVIDIA RTX A4000 Laptop

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 A4000 Laptop GPU comes equipped with 8 GB of GDDR6 graphics memory. This dedicated memory size determines which artificial intelligence models you can run entirely on the graphics hardware. Running a model fully within the video memory ensures the fastest possible processing speeds for text generation and image creation.

The quantization column shows the compression level applied to each model. Quantization reduces the size of a model so it fits into smaller memory spaces. For example, Mochi 1 10B fits into 7.3 GB of memory using a Q4_K_M quantization. Gemma 2 9B utilizes the maximum capacity of your hardware by using 8 GB of memory at the Q4_K_M quantization level.

Many popular models can run at higher precision levels on this hardware. Models like Granite 3.3 8B, Ministral 8B, and InternLM 3 8B fit into 7.9 GB of memory using a Q6_K quantization. You can also run the Mistral 7B model at Q6_K quantization using 7.4 GB of memory. Qwen2.5 7B and Falcon 3 7B fit comfortably at Q6_K quantization using 6.9 GB of memory.

When a model exceeds the 8 GB video memory limit, you must offload parts of it to your system RAM. This offloading 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 system RAM is slower than graphics memory. For instance, Gemma 3 12B and Mistral NeMo 12B require 8.8 GB of memory at Q4_K_M quantization and need 10.8 GB of system RAM.

Other large models also rely on this offloading method to function. Vicuna 13B needs 9.5 GB at Q4_K_M quantization and uses 11.5 GB of system RAM. Image generators like FLUX.1 dev require 14.4 GB at FP8 or optimized settings and use 16.4 GB of system RAM. Open-Sora 2.0 requires 8.1 GB at Q4_K_M quantization and uses 10.1 GB of system RAM.

Memory usage calculations assume a standard 4k context window. If you increase the context window to process longer documents, the model will require more graphics memory. This extra memory demand might force you to use a lower quantization level or offload more data to your system RAM to prevent out of memory errors.