Best local AI models for NVIDIA RTX 4500 Ada Generation

The largest models that fit fully

The 30 largest of the 173 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 4500 Ada Generation workstation graphics card features 24 GB of GDDR6 memory. This dedicated video memory determines the maximum size of the artificial intelligence models you can run locally. To run a model entirely on the graphics card, the model files and the active context data must fit within this 24 GB limit.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of model weights to save memory. For example, the Qwen3 32B, Qwen3.5 32B, Aya Expanse 32B, Granite 4.0 32B, and Qwen2.5-Coder 32B models fit within 23.4 GB of memory when compressed to the Q4_K_M quantization. Similarly, Qwen3-30B-A3B and Qwen3-Coder 30B-A3B fit within 22 GB at Q4_K_M.

Higher precision quantizations are available for slightly smaller models. Gemma 3 27B, Gemma 3 27B vision, and Wan 2.2 or 2.5 fit within 23 GB using the Q5_K_M quantization. Gemma 4 26B-A4B and other Gemma 4 sizes fit within 22.2 GB at Q5_K_M. Aria fits within 21.3 GB at Q5_K_M. Mistral Small 3.2, Magistral Small, and Devstral Small 1.1 fit within 23.6 GB using the Q6_K quantization. Solar Pro and Codestral 22B fit within 21.6 GB at Q6_K.

For maximum precision, you can run models at the Q8_0 quantization. HunyuanImage 2.1 or 3.0 fits within 21.6 GB at Q8_0. Ling-Coder-Lite fits within 21.4 GB at Q8_0. DeepSeek-Coder-V2 16B and Kimi-VL A3B fit within 20.4 GB at Q8_0. Apriel-1.5-15B-Thinker and StarCoder2 15B fit within 19.1 GB at Q8_0. Qwen2.5 14B fits within 19.5 GB at Q8_0.

If a model exceeds the 24 GB onboard memory, you must offload parts of the model to your system RAM. This offloading allows you to run larger models but reduces processing speed because system RAM is slower than GDDR6. For example, OTel 2.0 LLM 31B IT requires 27.5 GB at Q4_K_M and needs 29.5 GB of system RAM. DeepSeek-Coder 33B and WizardCoder 33B require 24.2 GB at Q4_K_M and need 26.2 GB of system RAM. Yi 1.5 34B, Granite Code 34B, LLaVA 1.5 or 1.6 34B, and Ovis 2 require 24.9 GB at Q4_K_M and need 26.9 GB of system RAM.

Other offload options include Qwen3.6-35B-A3B and Command R 35B, which require 25.6 GB at Q4_K_M and need 27.6 GB of system RAM. Seed-OSS 36B requires 26.4 GB at Q4_K_M and needs 28.4 GB of system RAM. All memory calculations in these lists assume a standard 4k context window. If you increase the context window to process longer documents, the model will require more memory, which may force you to use a lower quantization or rely on CPU offloading.