Best local AI models for AMD HD 7750

The largest models that fit fully

The 28 largest of the 28 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 AMD HD 7750 graphics card features 1 GB GDDR5 onboard memory. This memory size represents the total physical space available on the card for storing model weights and processing data. To run artificial intelligence models locally on this hardware, the model files must fit within this 1 GB limit. If a model exceeds this capacity, the system must use alternative execution methods.

The quantization column indicates the specific compression level used to shrink each model. Quantization reduces the precision of the model weights to save memory space. For example, Tango 2 at 1.4B parameters fits in 1 GB of memory using the Q4_K_M quantization. Smaller models like Qwen3 0.6B can use the higher quality Q8_0 quantization while consuming 0.8 GB of memory. Very small models like StyleTTS 2 at 0.15B parameters can run at full FP16 precision using only 0.4 GB of memory.

Running models on a 1 GB card requires careful attention to context window limits. Using a 4k context window increases memory usage significantly during generation. For models like Gemma 3 1B or Llama 3.2 1B running at Q6_K quantization, the model weights consume 1 GB of memory. This leaves no remaining space on the card for context data, so you must keep context lengths short to avoid out of memory errors.

When a model is too large for the onboard memory, you can use CPU offload. This method splits the workload between your graphics card and your system RAM. We assume a system with 32 GB of system RAM for these setups. Offloading allows you to run larger architectures, but it slows down processing speeds because data must travel between the system RAM and the graphics card.

Several models run successfully using CPU offload. Stable Diffusion 1.5 requires 1.3 GB of memory at FP8 precision and uses 3.3 GB of system RAM. Models like Whisper Large v2, AudioGen, and AudioLDM 2 require 1.1 GB of memory at Q4_K_M quantization and use 3.1 GB of system RAM. Sana 1.6B requires 1.2 GB of memory at Q4_K_M quantization and uses 3.2 GB of system RAM.