Best local AI models for NVIDIA Quadro K4000M

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 K4000M is a legacy mobile workstation graphics card equipped with 4 GB GDDR5 memory. This dedicated video memory determines the maximum size of the artificial intelligence models you can run entirely on the hardware. To run a model without system slowdowns, the model files and the active working memory must fit completely within this 4 GB physical limit.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of model weights to save space. For example, a Q6_K quant represents a highly accurate six bit quantization, while Q4_K_M is a four bit quantization that trades some output quality for a smaller footprint. On this hardware, smaller models can run at higher precision like Q8_0, while larger models require tighter Q4_K_M compression to fit.

For models that fit entirely in the 4 GB video memory, you can run options like Lumina-Next or CogVideoX 2B / 5B at Q4_K_M quant using 3.7 GB. Other options include DeepSeek-VL2 at Q5_K_M quant using 3.8 GB, and Phi-3.5-vision at Q5_K_M quant using 3.6 GB. You can also run Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, Danube 3 4B, and Fish Speech 1.5 / OpenAudio S1 at Q6_K quant using 3.9 GB.

Additional fully local options include Phi-4-mini-instruct, Phi-3.5 Mini, and OmniGen / OmniGen2 at Q6_K quant using 3.7 GB. For image generation, SD Cascade (Würstchen v3) fits at Q6_K quant using 3.5 GB, while SDXL Turbo and SDXL Lightning use 3.4 GB at Q6_K quant. Audio generation models like MusicGen small/medium/large fit at Q6_K quant using 3.2 GB. You can also run SmolLM3 3B, Replit Code v1.5 3B, Kandinsky 3.1, Voxtral Mini / Small, Orpheus TTS, and Higgs Audio v2 at Q8_0 quant using 3.8 GB.

When a model exceeds the 4 GB video memory, you must use CPU offload. This technique splits the model layers between your graphics card and your system RAM. Assuming a 32 GB system RAM setup, running Mistral 7B at Q4_K_M quant requires 5.7 GB of memory, which uses 7.7 GB of system RAM. Similarly, Qwen2.5 0.5B / 1.5B / 3B / 7B, OLMo 2 1B / 7B, Falcon 3 1B / 3B / 7B, Command R7B, and OpenHermes 2.5 at Q4_K_M quant require 5.1 GB of memory and use 7.1 GB of system RAM. Offloading allows these larger models to run, but it drastically reduces processing speed.

Be aware of the context window limit when running models locally. The memory figures listed are calculated at a standard 4k context window. If you increase the context length to process longer documents or extended conversations, the memory requirements will rise. This extra memory demand can push a model past the 4 GB threshold of your NVIDIA Quadro K4000M, forcing slow system RAM offloading even for smaller models.