Best local AI models for NVIDIA RTX A4500 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 A4500 Laptop graphics card features 16 GB of GDDR6 dedicated memory. This memory size determines which artificial intelligence models can run entirely on your local hardware. When a model fits completely within this video memory, it processes tokens at maximum speed. If a model exceeds this capacity, it cannot load or must offload layers to your system memory.

Quantization is a method that compresses model weights to save space. The quant column shows the best balance of size and quality for your hardware. For example, gpt-oss-20b and Reka Flash 3 are 21B models that fit in 15.4 GB of video memory using the Q4_K_M quantization. Other models like Qwen-Image and Qwen-Image-Edit require 14.6 GB of video memory at the same Q4_K_M level. CogVLM2 fits its 19B size into 13.9 GB using Q4_K_M.

Higher quantization levels like Q5_K_M and Q6_K offer better precision but require more space. HunyuanImage 2.1 / 3.0 uses 14.5 GB at Q5_K_M, while Ling-Coder-Lite uses 14.3 GB at Q5_K_M. At the Q6_K level, DeepSeek-Coder-V2 16B / 236B and Kimi-VL A3B both use 15.7 GB of video memory. Apriel-1.5-15B-Thinker and StarCoder2 3B / 7B / 15B fit into 14.8 GB at Q6_K. Qwen2.5 14B fits in 15.3 GB at Q6_K, while Phi-3 Medium, Phi-4, Phi-4-reasoning / -plus, Wan 2.2 T2I, Wan 2.1 (1.3B / 14B), and SkyReels V2 all require 13.8 GB at Q6_K.

Smaller models can run at the high Q8_0 quantization level for maximum accuracy. Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, FLUX.1 schnell, and FLUX.1 Kontext dev all use 15.3 GB of video memory at Q8_0. Vicuna 13B, HunyuanVideo, HunyuanVideo-Avatar, LTX-Video / LTX-2, and FramePack use 12.8 GB at Q6_K. FLUX.1 dev uses 14.4 GB with the FP8 / optimized quantization.

When a model is too large for the 16 GB video memory, you can offload parts of it to your system RAM. This offload process allows you to run larger models but reduces generation speed significantly. For these cases, we assume your laptop has 32 GB of system RAM. Solar Pro and Codestral 22B need 16.1 GB of memory at Q4_K_M and require 18.1 GB of system RAM. Mistral Small 3.2, Magistral Small, and Devstral Small 1.1 need 17.6 GB at Q4_K_M and require 19.6 GB of system RAM. Aria needs 18.3 GB at Q4_K_M and requires 20.3 GB of system RAM.

Even larger models can run with system RAM offloading. Gemma 4 26B-A4B and Gemma 4 (all sizes) need 19 GB at Q4_K_M and require 21 GB of system RAM. Gemma 3 27B and Gemma 3 4B/12B/27B (vision) need 19.8 GB at Q4_K_M and require 21.8 GB of system RAM. Keep in mind that these memory calculations are based on a standard 4k context window. Running longer conversations or larger prompts increases memory usage and may require you to use smaller models to avoid running out of video memory.