Best local AI models for NVIDIA RTX PRO 4500 Blackwell

The largest models that fit fully

The 30 largest of the 183 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 PRO 4500 Blackwell workstation graphics card features 32 GB of high speed GDDR7 memory. This dedicated onboard memory determines the maximum size of the artificial intelligence models you can run locally. To load a model completely onto the graphics hardware for fast execution the total memory footprint of the model must remain under this 32 GB limit.

The best quant column indicates the highest quality quantization level that fits within the hardware limits. Quantization compresses model weights to save space. A Q5_K_M quant offers an excellent balance of speed and accuracy while a Q6_K or Q8_0 quant provides even higher fidelity. For example the Seed-OSS 36B model fits at Q5_K_M using 30.7 GB of memory. The Qwen3 32B and Qwen3.5 dense variants fit at Q6_K using 31.5 GB of memory. Mistral Small 3.2 fits at Q8_0 using 30.5 GB of memory.

Several other large models run efficiently within the onboard memory. Qwen3.6-35B-A3B and Command R (35B) both fit at Q5_K_M using 29.8 GB of memory. Yi 1.5 34B and Granite Code 34B fit at Q5_K_M using 29 GB of memory. LLaVA 1.5 / 1.6 34B and Ovis 2 also fit at Q5_K_M using 29 GB of memory. DeepSeek-Coder 33B and WizardCoder 33B fit at Q5_K_M using 28.1 GB of memory. OTel 2.0 LLM 31B IT fits at Q5_K_M using 31.4 GB of memory.

Medium sized models can run at higher quantization levels. Aya Expanse 32B and Granite 4.0 Small/Tiny 32B fit at Q6_K using 31.5 GB of memory. Qwen2.5-Coder 32B fits at Q6_K using 31.5 GB of memory. Qwen3-30B-A3B and Qwen3-Coder 30B-A3B fit at Q6_K using 29.5 GB of memory. Gemma 3 27B and Wan 2.2 / 2.5 27B fit at Q6_K using 26.6 GB of memory. Gemma 4 26B-A4B fits at Q6_K using 25.6 GB of memory. Aria fits at Q8_0 using 31.8 GB of memory. Solar Pro 22B and Codestral 22B fit at Q8_0 using 28 GB of memory. Reka Flash 3 21B fits at Q8_0 using 26.7 GB of memory.

When a model is too large for the 32 GB graphics memory you can offload parts of it to your system RAM. This process allows you to run larger models but it reduces processing speed. For example Mixtral 8x7B requires 34.4 GB at Q4_K_M and needs 36.4 GB of system RAM. Llama 3.1 Nemotron 51B requires 37.3 GB at Q4_K_M and needs 39.3 GB of system RAM. Jamba 1.5 Mini / Large requires 38.1 GB at Q4_K_M and needs 40.1 GB of system RAM.

All memory calculations assume a standard 4k context window. Generating longer responses or processing larger documents increases memory usage. If you increase the context window beyond 4k tokens the model may exceed the 32 GB limit and require CPU offloading.