Best local AI models for AMD E6465

The largest models that fit fully

The 30 largest of the 56 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 E6465 is an embedded graphics processor equipped with 2 GB of GDDR5 memory. This dedicated memory size dictates the maximum size of the artificial intelligence models you can run entirely on the hardware. When a model fits completely within this 2 GB boundary, it executes with the highest possible throughput because the system does not need to transfer weights over the slower system bus.

To fit modern models into this memory limit, quantization is used to compress the model weights. The quantization column indicates the optimal format for each model. For example, the Allegro 2.8B model uses the Q4_K_M quantization to fit into 2 GB of memory. Smaller models like the SmolLM2 1.7B can run at a higher precision Q6_K quantization while using 1.7 GB of memory. TinyLlama 1.1B runs at Q8_0 quantization and uses 1.4 GB of memory.

If a model exceeds the 2 GB physical limit of the graphics card, you must use CPU offload. This technique stores a portion of the model weights in your system RAM. Running a model like SmolLM3 3B requires 2.2 GB of video memory at Q4_K_M quantization and also needs 4.2 GB of system RAM. Similarly, Stable Diffusion XL requires 4.1 GB at FP8 or optimized quantization, which demands 6.1 GB of system RAM.

CPU offload allows you to run larger architectures, but it introduces a performance cost. Transferring data between the 32 GB system RAM and the graphics card slows down processing speeds. While models like MusicGen medium at 3.3B or SDXL Turbo at 3.5B can run via offloading, their generation speeds will be lower than models that fit entirely within the local GDDR5 memory.

You must also consider the memory cost of context length during text generation. Running a model at its maximum 4k context window requires additional memory for the key value cache. This extra memory usage is not included in the static model weights. If you run a model near the 2 GB limit, you may need to reduce the context length to prevent out of memory errors.