Best local AI models for NVIDIA Quadro P600

The largest models that fit fully

The 30 largest of the 56 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 P600 is an entry level professional graphics card equipped with 2 GB of GDDR5 memory. This dedicated video memory is the primary constraint when running local artificial intelligence models. To run a model entirely on the GPU, the model files and active memory must fit within this 2 GB limit. If a model exceeds this capacity, you must utilize system memory offloading which slows down execution speed.

Quantization is a compression technique that reduces the precision of model weights to save memory. The quant column indicates the best format that fits your hardware. For the Quadro P600, smaller models can run at higher precision like Q8_0 or Q6_K. Larger models require tighter compression such as Q5_K_M or Q4_K_M to fit inside the 2 GB physical limit. Using a Q4_K_M quantization allows you to run larger architectures at the cost of some output accuracy.

Several highly optimized models fit entirely within the 2 GB GDDR5 frame buffer. The Allegro 2.8B model is the largest fully fitting option using exactly 2 GB of memory at Q4_K_M. Other compatible models include Open-Sora Plan 2.7B at Q4_K_M, LFM2 2.6B using 1.9 GB, and Playground v2.5 2.6B using 1.9 GB. You can also run Stable Diffusion 3.5 Medium 2.5B using 1.8 GB, or Canary 2.5B using 1.8 GB. These models run completely on the GPU for maximum possible speed.

For audio and vision tasks, the Quadro P600 supports SeamlessM4T v2 2.3B using 2 GB at Q5_K_M, and Parler-TTS 2.2B using 1.9 GB. Kimi K3 DSpark 2.2B fits using 2 GB. Highly compressed 2B models like SmolVLM, Stable Diffusion 3 Medium, Pyramid Flow, and Wav2Vec2 XLS-R fit using 2 GB at Q6_K. Extremely lightweight options like TinyLlama 1.1B and SantaCoder 1.1B run comfortably at Q8_0 precision using only 1.4 GB of video memory.

When a model is too large for the 2 GB video memory, you can offload layers to your system RAM. This requires a system with sufficient memory, such as 32 GB system RAM. For example, SmolLM3 3B, Replit Code v1.5 3B, Kandinsky 3.1, Voxtral Mini, Orpheus TTS, and Higgs Audio v2 all need 2.2 GB at Q4_K_M, which requires offloading 4.2 GB to system RAM. MusicGen small medium large 3.3B needs 2.4 GB at Q4_K_M and uses 4.4 GB of system RAM. SDXL Turbo 3.5B and SDXL Lightning 3.5B need 2.6 GB at Q4_K_M and require 4.6 GB of system RAM.

Heavy image generation models also require offloading on this hardware. Stable Diffusion XL 3.417B needs 4.1 GB at FP8 or optimized settings, which requires 6.1 GB of system RAM. While offloading enables you to run these larger models, the transfer of data between the system RAM and the GPU over the PCIe bus creates a performance bottleneck. This bottleneck significantly increases generation times compared to models that fit entirely within the local GDDR5 memory.

You must also consider the context window when running text models. The memory figures listed are for the base model weights only. Running a model with a standard 4k context window requires additional memory to store active tokens. If you push the context window to its limit, the active memory will spill over the 2 GB threshold. To prevent crashes or severe slowdowns, you may need to reduce the context length or use a smaller model.