Best local AI models for NVIDIA RTX A5500 Laptop

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 A5500 Laptop GPU features 16 GB of GDDR6 memory. This dedicated memory size determines the maximum size of the artificial intelligence models you can run locally. To run a model entirely on your graphics hardware, the model files and the active context must fit within this 16 GB limit. Keeping the model inside the video memory ensures the fastest possible processing speeds.

The quantization column indicates the compression level applied to each model. Raw models are often too large for laptop hardware, so they are compressed into smaller formats like Q4_K_M, Q5_K_M, Q6_K, or Q8_0. For example, the 21B parameter gpt-oss-20b and Reka Flash 3 models fit within 15.4 GB of video memory when using the Q4_K_M quantization. Models like Qwen-Image and Qwen-Image-Edit fit at Q4_K_M using 14.6 GB of memory.

As model parameters decrease, you can use higher quality quantizations. The 17B HunyuanImage 2.1 / 3.0 fits at Q5_K_M using 14.5 GB of memory. The 15B StarCoder2 3B / 7B / 15B and Apriel-1.5-15B-Thinker models run at Q6_K using 14.8 GB of memory. Popular 14B models such as Phi-4, Phi-4-reasoning / -plus, Wan 2.2 T2I, Wan 2.1 (1.3B / 14B), and SkyReels V2 also run at Q6_K using 13.8 GB of memory.

For 12B models, you can run the highest quality Q8_0 quantization. Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, and FLUX.1 schnell use 15.3 GB of video memory at Q8_0. The FLUX.1 dev model can run at FP8 / optimized using 14.4 GB of video memory. Note that these memory calculations are based on a standard 4k context window. Increasing the context window size will require more memory and may force you to use smaller models.

If you want to run larger models, you can offload part of the workload to your system RAM. This offloading process slows down processing speeds significantly. Assuming your laptop has 32 GB of system RAM, you can run the 22B Solar Pro or Codestral 22B at Q4_K_M, which requires 16.1 GB of video memory and 18.1 GB of system RAM. You can also run Mistral Small 3.2, Magistral Small, or Devstral Small 1.1 at Q4_K_M using 17.6 GB of video memory and 19.6 GB of system RAM.

Even larger models are accessible through offloading. The 25B Aria model requires 18.3 GB of video memory and 20.3 GB of system RAM at Q4_K_M. The 26B Gemma 4 (all sizes) and Gemma 4 26B-A4B require 19 GB of video memory and 21 GB of system RAM. Finally, the 27B Gemma 3 27B and Gemma 3 4B/12B/27B (vision) models can run at Q4_K_M by using 19.8 GB of video memory and 21.8 GB of system RAM.