Best local AI models for NVIDIA RTX A1000

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 A1000 is a professional graphics card equipped with 8 GB of GDDR6 memory. This dedicated memory size determines which artificial intelligence models can run entirely on your hardware. For local execution, the model weights and the active context window must fit within this 8 GB limit to maintain fast processing speeds.

To fit larger models into the available memory, developers use quantization. The quant column shows the specific compression level used for each model. For example, the Q4_K_M quant represents a four bit quantization level, while Q5_K_M and Q6_K represent five bit and six bit levels. Higher quantization levels preserve more of the original model accuracy but require more memory space.

Several highly capable models can run entirely within your local GPU memory. The Mochi 1 10B model fits using the Q4_K_M quant, which consumes 7.3 GB of memory. Gemma 2 9B utilizes the full 8 GB of memory at the Q4_K_M quant. Other models like Nemotron Nano 9B, GLM-4 9B, Yi-Coder 9B, and GLM-4V-9B fit comfortably using the Q5_K_M quant, requiring 7.7 GB of memory.

Popular 8B and 7B models also run efficiently on this hardware. Llama 3.1 8B fits at the Q5_K_M quant using 7.4 GB of memory. Granite 3.3 8B, Ministral 8B, and Stable Diffusion 3.5 Large run at the Q6_K quant using 7.9 GB of memory. Standard 7B models like Mistral 7B require 7.4 GB of memory at Q6_K, while Qwen2.5 7B and Falcon 3 7B require 6.9 GB of memory at the same Q6_K quant.

When a model exceeds the 8 GB GPU memory limit, you can use CPU offloading if your computer has at least 32 GB of system RAM. This process splits the workload between your graphics card and system memory. For instance, Gemma 3 12B and Mistral NeMo 12B require 8.8 GB of memory at Q4_K_M, which utilizes 10.8 GB of system RAM. FLUX.1 dev requires 14.4 GB at FP8 and utilizes 16.4 GB of system RAM. Offloading allows you to run these larger models, but it reduces processing speed.

All memory calculations for these local models assume a standard 4k context window. If you increase the context length to process longer documents or extended conversations, the active memory usage will rise. This extra memory demand may require you to use a lower quantization level or rely on CPU offloading to prevent out of memory errors.