Best local AI models for NVIDIA RTX 3070 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 3070 Laptop graphics card features 8 GB of GDDR6 dedicated memory. This physical limit determines which artificial intelligence models can run entirely on your hardware. To run a model smoothly, the model files and the active workspace must fit inside this memory space. If a model exceeds this limit, your system must use slower memory types.

The quantization column shows the compression level used to shrink these files. 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 larger file sizes. Choosing the best quant allows you to run larger models like Mochi 1 at Q4_K_M using 7.3 GB of memory, or Gemma 2 9B at Q4_K_M using exactly 8 GB of memory.

Many capable models fit fully within your hardware limits. You can run Nemotron Nano 9B, GLM-4 9B, GLM-4.5-Air, Yi-Coder 9B, GLM-4-9B-Chat, CodeGeeX4, GLM-4V-9B, and GLM-4.1V-Thinking at Q5_K_M quantization using 7.7 GB of memory. Chroma 8.9B 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, MiniCPM-o 2.6, Idefics 3 8B, Fuyu-8B, Emu3, and Stable Diffusion 3.5 Large or Turbo fit at Q6_K using 7.9 GB of memory.

Slightly smaller models leave more room for processing. EXAONE 3.5 7.8B fits at Q6_K using 7.7 GB. Mistral 7B uses 7.4 GB at Q6_K. You can also 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 of memory. These options leave a safe buffer for your system display and basic operations.

When a model is too large for your graphics memory, you can offload parts of it to your system RAM. This process requires a system with 32 GB of system RAM. For example, Open-Sora 2.0 needs 8.1 GB at Q4_K_M and uses 10.1 GB of system RAM. FLUX.1 dev needs 14.4 GB at FP8 or optimized settings and uses 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 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. Offloading prevents out of memory crashes but reduces processing speed significantly.

You must also consider the context window size when planning your memory usage. The standard memory figures assume a basic context length of 4k tokens. If you increase the context window to process longer documents or chat histories, the memory usage will rise. Running models very close to the 8 GB limit might cause slowdowns if your active context grows too large.