Best local AI models for NVIDIA RTX 5070 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 RTX 5070 Laptop GPU features 8 GB of GDDR7 memory. This dedicated video memory is the primary space where your local AI models run. To achieve fast generation speeds, the entire active model should fit inside this 8 GB boundary. If a model exceeds this limit, your system must transfer data back and forth from your system RAM, which slows down performance.

The quantization column indicates the compression level used to shrink these models. Quantization reduces the precision of model weights to save space. For example, the Q4_K_M quant represents a medium four bit quantization, while Q5_K_M and Q6_K offer higher precision at the cost of more memory. A higher quant level preserves more of the original model intelligence but leaves less free space in your video memory.

For models that fit entirely within your 8 GB of video memory, you can run highly capable options. Mochi 1 at 10B fits using the Q4_K_M quant with 7.3 GB used. Gemma 2 9B utilizes exactly 8 GB at Q4_K_M. You can also run Nemotron Nano 9B, GLM-4 9B, Yi-Coder 9B, GLM-4-9B-Chat, and GLM-4V-9B using Q5_K_M quants which consume 7.7 GB. Chroma uses 7.6 GB at Q5_K_M. Llama 3.1 8B fits at Q5_K_M using 7.4 GB.

Other 8B models like 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 fit using the Q6_K quant with 7.9 GB used. EXAONE 3.5 7.8B uses 7.7 GB at Q6_K. Popular 7B models like Mistral 7B use 7.4 GB at Q6_K, while 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 use 6.9 GB at Q6_K.

When you want to run larger models, you must use CPU offloading to share the workload with your system RAM. Assuming you have 32 GB of system RAM, you can run Open-Sora 2.0 at Q4_K_M which needs 8.1 GB of video memory and 10.1 GB of system RAM. FLUX.1 dev needs 14.4 GB at FP8 and 16.4 GB of system RAM. Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, FLUX.1 schnell, FLUX.1 Kontext dev, and FLUX.1 Krea dev all need 8.8 GB at Q4_K_M and 10.8 GB of system RAM. Vicuna 13B needs 9.5 GB at Q4_K_M and 11.5 GB of system RAM.

Please note that these memory calculations are based on a standard 4k context window. As you type longer prompts and receive longer answers, the context window expands. This expansion requires additional video memory. If you plan to use very long conversations, you should choose a smaller model size to prevent running out of memory during generation.