Best local AI models for AMD HD 5470

The largest models that fit fully

The 12 largest of the 12 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 5470 graphics card features 0.5 GB of GDDR3 video memory. This hardware specification limits the size of local artificial intelligence models that you can run directly on the graphics processor. To execute a model entirely within the video memory, the total footprint of the model weights must not exceed this 0.5 GB threshold. This page lists the compatible models that fit within this hardware constraint.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of the model weights to save memory. For example, the Q5_K_M quant allows the Qwen3 0.6B model, the PixArt-α / PixArt-Σ model, and the Parakeet TDT 0.6B v2 model to fit exactly into 0.5 GB of video memory. Similarly, the XTTS v2, Spark-TTS, and CosyVoice 2 models utilize the Q6_K quant to operate within 0.5 GB. Highly compressed formats like Q8_0 allow the VALL-E X (unofficial) model to use 0.5 GB, while the ERNIE 4.5 open weights, F5-TTS, E2-TTS, and ChatTTS models require 0.4 GB. The StyleTTS 2 model runs at FP16 precision using 0.4 GB of video memory.

Running models with a 4k context window or higher requires additional memory for the context history. Because the AMD HD 5470 has only 0.5 GB of video memory, processing long text sequences will quickly exceed the hardware limits. Users must keep context lengths short to prevent out of memory errors during inference.

When a model is too large for the video memory, you can use CPU offload. This technique splits the model weights between the graphics card and your system memory. This process assumes your computer has 32 GB of system RAM. Offloading allows you to run larger models, but it reduces the processing speed because system RAM is slower than video memory.

Several models can run using CPU offload with this hardware setup. The Florence-2 base/large model and the Magenta RT model both require 0.6 GB of video memory at the Q4_K_M quant, which utilizes 2.6 GB of system RAM. The Stable Diffusion 2.1, Bark, Tortoise TTS, and Riffusion (SD-based) models require 0.7 GB of video memory at the Q4_K_M quant and utilize 2.7 GB of system RAM.

Other larger models also run via CPU offload under the same memory conditions. The Gemma 3 1B model, the Llama 3.2 1B / 3B model, the MMS (1100+ languages) model, and the CSM-1B model all require 0.7 GB of video memory at the Q4_K_M quant. These models utilize 2.7 GB of system RAM to complete their execution.