Best local AI models for NVIDIA Quadro K5000

The largest models that fit fully

The 30 largest of the 81 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 Quadro K5000 is an older professional workstation graphics card equipped with 4 GB of GDDR5 video memory. This onboard memory capacity dictates the maximum size of the artificial intelligence models you can run entirely on the hardware. To execute local models without system slowdowns, the model files and their active working memory must fit within this 4 GB limit.

Model quantization is a method that compresses the weight parameters of neural networks. The quantization column shows the best compression level that fits your hardware. For example, a Q4_K_M quantization uses approximately four bits per parameter, while Q6_K and Q8_0 use six and eight bits. Higher quantization levels preserve more original model intelligence but require more memory space.

With 4 GB GDDR5, you can run several optimized models entirely on the GPU. The largest fitting models include Lumina-Next or Lumina-Image 2.0 at 5B parameters using a Q4_K_M quantization which consumes 3.7 GB of video memory. You can also run DeepSeek-VL2 at 4.5B parameters with a Q5_K_M quantization using 3.8 GB, or Qwen3 4B with a Q6_K quantization using 3.9 GB.

Other fully local options include Phi-4-mini-instruct at 3.8B parameters using a Q6_K quantization for 3.7 GB of memory. Image generation models like SDXL Turbo at 3.5B parameters fit well using a Q6_K quantization that takes 3.4 GB. Audio models like Orpheus TTS at 3B parameters fit using a Q8_0 quantization that requires 3.8 GB of video memory.

When a model exceeds the 4 GB video memory limit, you must use CPU offload. This technique splits the model layers between your graphics card and your system RAM. Assuming you have 32 GB of system RAM, you can run larger models with a performance cost. For example, Mistral 7B at Q4_K_M quantization needs 5.7 GB of total memory, which requires 7.7 GB of system RAM to offload the extra layers.

Other offload options include Magicoder-S-DS 6.7B at Q4_K_M quantization needing 4.9 GB of memory and 6.9 GB of system RAM. Stable Diffusion XL at 3.417B parameters needs 4.1 GB at FP8 quantization and 6.1 GB of system RAM. Running models this way is slower because data must transfer between the system RAM and the graphics card.

You must also consider the context window size when loading these models. The memory numbers listed here assume a standard 4k context window. If you increase the context length to process longer documents or chat histories, the memory usage will rise quickly. This extra memory demand can cause a model that fits at startup to exceed your 4 GB limit during a long conversation.