Best local AI models for NVIDIA GTX 1650 Ti MAX-Q

The largest models that fit fully

The 30 largest of the 81 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 GTX 1650 Ti MAX-Q is a mobile graphics card equipped with 4 GB of GDDR6 memory. This dedicated video memory is the primary limiting factor when running artificial intelligence models locally. To achieve acceptable generation speeds, the model files must fit entirely within this 4 GB limit. If a model exceeds this capacity, the system must transfer data between the graphics card and system memory, which slows down performance.

Quantization is a compression method that reduces the size of these models. The quant column indicates the specific level of compression used to fit the model into your hardware. For example, a Q4_K_M quant uses approximately four bits per parameter, while a Q8_0 quant uses eight bits. Higher quantization levels like Q8_0 preserve more original model accuracy but require more memory. Lower quantization levels like Q4_K_M allow larger models to run within the 4 GB boundary.

For fully local execution on the graphics card, the largest fitting models range from 2.5B to 5B parameters. The 5B parameter models like Lumina-Next, Lumina-Image 2.0, and CogVideoX 2B or 5B can run using the Q4_K_M quant, which consumes 3.7 GB of memory. Vision models such as DeepSeek-VL2 and Phi-3.5-vision fit within the limit by using Q5_K_M quants. Multiple 4B models like Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, Danube 3 4B, and Fish Speech 1.5 or OpenAudio S1 can run at Q6_K quantization using 3.9 GB.

Other models under 4B parameters can run with higher precision quants. The Phi-4-mini-instruct, Phi-3.5 Mini, and OmniGen or OmniGen2 models use 3.7 GB of memory at Q6_K. Image generation models like SD Cascade (Würstchen v3), SDXL Turbo, and SDXL Lightning also fit within 3.5 GB using Q6_K. Models like SmolLM3 3B, Replit Code v1.5 3B, Kandinsky 3.1, Voxtral Mini or Small, Orpheus TTS, and Higgs Audio v2 can run at the higher quality Q8_0 quant using 3.8 GB.

When a model is too large for the 4 GB video memory, you can offload parts of the model to your system RAM. This CPU offload process requires a system with 32 GB of system RAM to handle the overflow. For instance, running Mistral 7B at Q4_K_M requires 5.7 GB of video memory and 7.7 GB of system RAM. Similarly, Qwen2.5 0.5B / 1.5B / 3B / 7B, OLMo 2 1B / 7B, Falcon 3 1B / 3B / 7B, Command R7B, and OpenHermes 2.5 each require 5.1 GB of video memory and 7.1 GB of system RAM at Q4_K_M.

Other offload options include Phi-4-multimodal, which needs 4.1 GB of video memory and 6.1 GB of system RAM. Magicoder-S-DS 6.7B requires 4.9 GB of video memory and 6.9 GB of system RAM. Stable Diffusion XL needs 4.1 GB of video memory and 6.1 GB of system RAM at FP8 or optimized settings. While offloading allows you to run these larger models, the transfer of data between the graphics card and system RAM reduces generation speed significantly.

Memory consumption calculations assume a standard 4k context window. As you input longer text prompts or generate longer responses, the context window expands and consumes additional video memory. If you write very long prompts, the memory usage will exceed the listed figures. This can cause the model to slow down or fail to run if the total memory exceeds the 4 GB limit of your graphics card.