Best local AI models for NVIDIA RTX 5060

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 RTX 5060 graphics card features 8 GB of GDDR7 memory. This onboard memory determines the size of the artificial intelligence models you can run locally. To run a model entirely on your graphics card for maximum speed, the model files and active memory must fit within this 8 GB limit. If a model exceeds this limit, your system must use alternative execution methods.

The quantization column shows the compression level used to fit these models into your hardware. Quantization reduces the precision of model weights to save space. A Q4_K_M quant represents a four bit quantization level that balances size and accuracy. A Q6_K quant uses six bits to provide higher precision but requires more memory. For example, Mochi 1 at 10B parameters fits at Q4_K_M using 7.3 GB of memory, while Mistral 7B can run at a higher precision Q6_K quant using 7.4 GB.

Many popular models fit comfortably within the 8 GB limit of your card. Gemma 2 9B runs at Q4_K_M using exactly 8 GB. You can run Nemotron Nano 9B, GLM-4.5-Air, Yi-Coder 9B, GLM-4-9B-Chat, and GLM-4.1V-Thinking at Q5_K_M using 7.7 GB. Chroma fits at Q5_K_M using 7.6 GB. Llama 3.1 8B runs at Q5_K_M using 7.4 GB. 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 all run at Q6_K using 7.9 GB.

Slightly smaller models offer even more memory headroom. EXAONE 3.5 7.8B uses 7.7 GB at Q6_K. You can run 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 at Q6_K using 6.9 GB. These models leave more space for system tasks and longer conversations.

When a model is too large for your graphics card, you can use CPU offload if you have 32 GB of system RAM. This process splits the model between your graphics card and system memory. Offloading makes larger models run but slows down generation speeds. For example, Open-Sora 2.0 at 11B parameters needs 8.1 GB at Q4_K_M and uses 10.1 GB of system RAM. FLUX.1 dev at 12B needs 14.4 GB at FP8 and uses 16.4 GB of system RAM.

Other offload options include Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, FLUX.1 schnell, FLUX.1 Kontext dev, and FLUX.1 Krea dev. These 12B models all need 8.8 GB at Q4_K_M and use 10.8 GB of system RAM. Vicuna 13B needs 9.5 GB at Q4_K_M and uses 11.5 GB of system RAM. Keep in mind that all memory calculations assume a standard 4k context window. Running longer context windows increases memory usage and may require lower quantization levels.