Best local AI models for NVIDIA Quadro RTX 4000 Laptop

The largest models that fit fully

The 30 largest of the 123 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 RTX 4000 Laptop GPU features 8 GB of GDDR6 memory. This dedicated video memory determines the size of the artificial intelligence models you can run locally. When you run a model entirely on your graphics hardware, the model weights must fit inside this 8 GB limit. Keeping the entire model on your GPU ensures the fastest processing speeds for text generation and image creation.

The quantization column indicates the compression level used to fit these models into your hardware memory. Quantization reduces the precision of model weights to save space. For example, a Q4_K_M quantization uses a four bit format to compress larger models. A Q6_K quantization uses a six bit format which preserves more original model quality but requires more memory per parameter.

Several high quality models fit completely within your 8 GB limit. The Mochi 1 10B model fits at Q4_K_M quantization using 7.3 GB of memory. You can also run Gemma 2 9B at Q4_K_M using 8 GB of memory. Models like Nemotron Nano 4B / 9B, GLM-4 9B / GLM-4.5-Air, Yi-Coder 1.5B / 9B, GLM-4-9B-Chat / CodeGeeX4, and GLM-4V-9B / GLM-4.1V-Thinking fit at Q5_K_M quantization using 7.7 GB of memory. Chroma 8.9B fits at Q5_K_M using 7.6 GB of memory, while Llama 3.1 8B fits at Q5_K_M using 7.4 GB of memory.

Other models can run at the higher quality Q6_K quantization. Granite 3.3 2B / 8B, Ministral 3B / 8B, InternLM 3 8B, OpenCoder 1.5B / 8B, Seed-Coder 8B, MiniCPM-V 2.6 / MiniCPM-o 2.6, Idefics 3 8B, Fuyu-8B, Emu3, and Stable Diffusion 3.5 Large / Turbo all use 7.9 GB of memory. EXAONE 3.5 2.4B / 7.8B uses 7.7 GB of memory. Mistral 7B uses 7.4 GB of memory. Qwen2.5 0.5B / 1.5B / 3B / 7B, OLMo 2 1B / 7B, Falcon 3 1B / 3B / 7B, Command R7B, OpenHermes 2.5, Zephyr 7B Beta, OpenChat 3.5, Starling LM 7B, and Codestral Mamba 7B all use 6.9 GB of memory.

When a model is too large for your 8 GB video memory, you can offload parts of it to your system RAM. This process requires a system with 32 GB of system RAM. Offloading allows you to run larger models but slows down processing speeds significantly. For example, FLUX.1 dev 12B requires 14.4 GB at FP8 / optimized quantization and needs 16.4 GB of system RAM. Open-Sora 2.0 11B needs 8.1 GB at Q4_K_M quantization and 10.1 GB of system RAM.

Other offload options include Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, FLUX.1 schnell 12B, FLUX.1 Kontext dev 12B, and FLUX.1 Krea dev 12B. These models all need 8.8 GB at Q4_K_M quantization and 10.8 GB of system RAM. Vicuna 13B needs 9.5 GB at Q4_K_M quantization and 11.5 GB of system RAM. Note that memory calculations assume a standard 4k context window. Increasing the context window size to process longer texts will require additional memory and may exceed your limits.