Best local AI models for AMD RX 550

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 RX 550 is an entry level graphics card equipped with 2 GB GDDR5 VRAM. This memory size is the strict limit for running local AI models entirely on the GPU. If a model and its runtime data exceed this 2 GB threshold, the system must offload parts of the workload to your system RAM. While this card has hardware limitations, you can still run several optimized small language models, image generators, and audio tools locally.

To fit models into the limited 2 GB VRAM, you must use quantized versions. The quant column indicates the compression level applied to the model weights. For example, a Q4_K_M quant reduces weights to approximately four bits, which allows larger models to fit into smaller memory spaces. Higher quants like Q6_K or Q8_0 offer better output quality but require more VRAM. Choosing the right quant is essential to balance output accuracy with the hardware limits of your card.

Several models can run fully within the 2 GB VRAM limit of your card. The largest fitting models include Allegro at 2.8B using a Q4_K_M quant with 2 GB used, and Open-Sora Plan at 2.7B using a Q4_K_M quant with 2 GB used. Other options are LFM2 2.6B and Playground v2.5 2.6B, which both use 1.9 GB of VRAM at Q4_K_M. You can also run Stable Diffusion 3.5 Medium at 2.5B or Canary 2.5B, which both require 1.8 GB of VRAM at Q4_K_M.

For audio and vision tasks, you can deploy SeamlessM4T v2 at 2.3B using a Q5_K_M quant with 2 GB used, or Parler-TTS at 2.2B using a Q5_K_M quant with 1.9 GB used. Highly compressed models like SmolVLM 2B, Stable Diffusion 3 Medium 2B, Pyramid Flow 2B, and Wav2Vec2 XLS-R 2B all run at Q6_K using exactly 2 GB of VRAM. Smaller models such as Qwen3 1.7B and SmolLM2 1.7B fit easily at Q6_K using 1.7 GB of VRAM.

When you run larger models, you must use CPU offload to system RAM. Assuming you have 32 GB of system RAM, you can run SmolLM3 3B, Replit Code v1.5 3B, Kandinsky 3.1, Voxtral Mini Small, Orpheus TTS, or Higgs Audio v2. These 3B models need 2.2 GB of VRAM at Q4_K_M and 4.2 GB of system RAM. You can also run MusicGen small/medium/large 3.3B, which needs 2.4 GB at Q4_K_M and 4.4 GB of system RAM. SDXL Turbo 3.5B and SDXL Lightning 3.5B need 2.6 GB at Q4_K_M and 4.6 GB of system RAM. Stable Diffusion XL 3.417B needs 4.1 GB at FP8 and 6.1 GB of system RAM.

Offloading models to system RAM comes with a performance cost. System RAM is much slower than GDDR5 VRAM, which reduces generation speed. Additionally, you must consider the 4k context caveat. Running text models with a 4000 token context window increases memory usage during generation. This extra memory pressure can push a model that fits at idle over the 2 GB VRAM limit, which forces unexpected offloading and slows down your system.