Best local AI models for NVIDIA Quadro P4200 MAX-Q

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 P4200 MAX-Q is a professional mobile graphics card equipped with 8 GB of GDDR5 video memory. This onboard memory determines the maximum size of the artificial intelligence models you can run entirely on the hardware. Keeping a model fully within this video memory is critical because it ensures the fastest processing speeds and prevents severe performance bottlenecks.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of model weights to save space. For example, a Q4_K_M quant uses a four bit quantization method, while a Q6_K quant uses a six bit method. Higher quantization levels like Q6_K preserve more of the original model quality but require more video memory, whereas Q4_K_M allows larger models to fit into your 8 GB limit.

Several high quality models fit directly into the 8 GB video memory of this GPU. The Mochi 1 10B model fits at a Q4_K_M quantization using 7.3 GB of memory. Gemma 2 9B fits at Q4_K_M using exactly 8 GB. You can also run Nemotron Nano 9B, GLM-4 9B, Yi-Coder 9B, GLM-4-9B-Chat, and GLM-4V-9B at a Q5_K_M quantization, with each using 7.7 GB of video memory. Chroma 8.9B fits at Q5_K_M using 7.6 GB, and Llama 3.1 8B fits at Q5_K_M using 7.4 GB.

Other excellent options include Granite 3.3 8B, Ministral 8B, InternLM 3 8B, OpenCoder 8B, Seed-Coder 8B, MiniCPM-V 2.6, Idefics 3 8B, Fuyu-8B, Emu3, and Stable Diffusion 3.5 Large. These models run at a high Q6_K quantization and use 7.9 GB of video memory. EXAONE 3.5 7.8B also runs at Q6_K using 7.7 GB. Popular 7B models like Mistral 7B, Qwen2.5 7B, OLMo 2 7B, Falcon 3 7B, Command R7B, OpenHermes 2.5, Zephyr 7B Beta, OpenChat 3.5, Starling LM 7B, and Codestral Mamba 7B fit comfortably at Q6_K using between 6.9 GB and 7.4 GB.

When a model exceeds the 8 GB video memory limit, you must offload a portion of the workload to your system RAM. This offloading process allows you to run larger models but slows down generation speeds significantly. For instance, Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, FLUX.1 schnell, FLUX.1 Kontext dev, and FLUX.1 Krea dev require 8.8 GB at Q4_K_M and need 10.8 GB of system RAM. FLUX.1 dev requires 14.4 GB at FP8 and needs 16.4 GB of system RAM. Vicuna 13B requires 9.5 GB at Q4_K_M and needs 11.5 GB of system RAM.

You must also consider the context window when planning your memory usage. The memory figures listed here are calculated using a standard 4k context window. If you increase the context window to process longer documents or conversations, the model will require significantly more video memory. This extra memory demand may force you to use a lower quantization level or offload parts of the model to system RAM.