Best local AI models for NVIDIA Quadro K4000

The largest models that fit fully

The 30 largest of the 76 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 K4000 is an older workstation graphics card equipped with 3 GB of GDDR5 memory. This memory capacity is the main limiting factor when running local artificial intelligence models. To run a model entirely on this GPU, the model files and the active memory space must fit within this 3 GB limit. If a model exceeds this size, it cannot run purely on the graphics hardware.

Quantization is a compression method that reduces the memory footprint of neural networks. The quant column shows the best format that fits within your hardware limits. For example, Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, and Danube 3 4B can run at Q4_K_M quantization while using 2.9 GB of memory. Similarly, Fish Speech 1.5 / OpenAudio S1 fits at Q4_K_M using 2.9 GB. Models like Phi-4-mini-instruct, Phi-3.5 Mini, and OmniGen / OmniGen2 fit at Q4_K_M using 2.8 GB. SD Cascade (Würstchen v3) uses 2.6 GB at Q4_K_M.

Higher quantization levels offer better precision but require more memory per parameter. SDXL Turbo, SDXL Lightning, and ACE-Step run at Q5_K_M using 3 GB of memory. MusicGen small/medium/large fits at Q5_K_M using 2.8 GB. You can use Q6_K quantization for SmolLM3 3B, Replit Code v1.5 3B, Kandinsky 3.1, Voxtral Mini / Small, Orpheus TTS, and Higgs Audio v2, which all use 3 GB. Allegro uses 2.8 GB at Q6_K, while Open-Sora Plan uses 2.7 GB. LFM2 1.2B / 2.6B and Playground v2.5 use 2.6 GB at Q6_K. Stable Diffusion 3.5 Medium and Canary 1B / Qwen-2.5B use 2.5 GB at Q6_K.

For maximum precision, some smaller models can run at Q8_0 quantization. SeamlessM4T v2 uses 2.9 GB at Q8_0. Parler-TTS uses 2.8 GB at Q8_0. Kimi K3 DSpark uses 2.9 GB at Q8_0. SmolVLM 256M / 500M / 2B uses 2.5 GB at Q8_0. When running these models, you must monitor your context window. Using a standard 4k context window increases memory usage. If you experience out of memory errors, you must lower the context length to keep the model within the 3 GB limit.

If you want to run larger models, you must use CPU offloading. This process splits the workload between your GPU and your system RAM. We assume you have 32 GB of system RAM for these cases. Offloading allows you to run models that exceed 3 GB, but it reduces processing speed because system RAM is much slower than GDDR5 graphics memory.

With CPU offload, you can run Stable Diffusion XL which needs 4.1 GB at FP8 / optimized and 6.1 GB of system RAM. Phi-3 Mini needs 4.4 GB at Q4_K_M and 6.4 GB of system RAM. Phi-3.5-vision and DeepFloyd IF both need 3.1 GB at Q4_K_M and 5.1 GB of system RAM. DeepSeek-VL2 needs 3.3 GB at Q4_K_M and 5.3 GB of system RAM. Lumina-Next / Lumina-Image 2.0 and CogVideoX 2B / 5B need 3.7 GB at Q4_K_M and 5.7 GB of system RAM. Phi-4-multimodal needs 4.1 GB at Q4_K_M and 6.1 GB of system RAM. Magicoder-S-DS 6.7B needs 4.9 GB at Q4_K_M and 6.9 GB of system RAM. Mistral 7B needs 5.7 GB at Q4_K_M and 7.7 GB of system RAM.