Best local AI models for AMD RX 550X
4 GB GDDR5. At a 4k context, 81 of the 233 models in our catalog with verified parameter counts fit fully, up to Lumina-Next / Lumina-Image 2.0 at 5B parameters.
Check your own machine against every model →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.
| Model | Parameters | Best quant that fits | Memory used at 4k |
|---|---|---|---|
| Lumina-Next / Lumina-Image 2.0 | 5B | Q4_K_M | 3.7 GB |
| CogVideoX 2B / 5B | 5B | Q4_K_M | 3.7 GB |
| DeepSeek-VL2 | 4.5B | Q5_K_M | 3.8 GB |
| DeepFloyd IF | 4.3B | Q5_K_M | 3.7 GB |
| Phi-3.5-vision | 4.2B | Q5_K_M | 3.6 GB |
| Qwen3 4B | 4B | Q6_K | 3.9 GB |
| Gemma 3 4B | 4B | Q6_K | 3.9 GB |
| Gemma 4 E4B | 4B | Q6_K | 3.9 GB |
| MiniCPM 3 4B | 4B | Q6_K | 3.9 GB |
| Danube 3 4B | 4B | Q6_K | 3.9 GB |
| Fish Speech 1.5 / OpenAudio S1 | 4B | Q6_K | 3.9 GB |
| Phi-4-mini-instruct | 3.8B | Q6_K | 3.7 GB |
| Phi-3.5 Mini | 3.8B | Q6_K | 3.7 GB |
| OmniGen / OmniGen2 | 3.8B | Q6_K | 3.7 GB |
| SD Cascade (Würstchen v3) | 3.6B | Q6_K | 3.5 GB |
| SDXL Turbo | 3.5B | Q6_K | 3.4 GB |
| SDXL Lightning | 3.5B | Q6_K | 3.4 GB |
| ACE-Step | 3.5B | Q6_K | 3.4 GB |
| MusicGen small/medium/large | 3.3B | Q6_K | 3.2 GB |
| SmolLM3 3B | 3B | Q8_0 | 3.8 GB |
| Replit Code v1.5 3B | 3B | Q8_0 | 3.8 GB |
| Kandinsky 3.1 | 3B | Q8_0 | 3.8 GB |
| Voxtral Mini / Small | 3B | Q8_0 | 3.8 GB |
| Orpheus TTS | 3B | Q8_0 | 3.8 GB |
| Higgs Audio v2 | 3B | Q8_0 | 3.8 GB |
| Allegro | 2.8B | Q8_0 | 3.6 GB |
| Open-Sora Plan | 2.7B | Q8_0 | 3.4 GB |
| LFM2 1.2B / 2.6B | 2.6B | Q8_0 | 3.3 GB |
| Playground v2.5 | 2.6B | Q8_0 | 3.3 GB |
| Stable Diffusion 3.5 Medium | 2.5B | Q8_0 | 3.2 GB |
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.
| Model | Parameters | Memory at FP8 / optimized | System RAM at 4k |
|---|---|---|---|
| Stable Diffusion XL | 3.417B | 4.1 GB needed | 6.1 GB |
| Phi-3 Mini | 3.8B | 4.4 GB needed | 6.4 GB |
| Phi-4-multimodal | 5.6B | 4.1 GB needed | 6.1 GB |
| Magicoder-S-DS 6.7B | 6.7B | 4.9 GB needed | 6.9 GB |
| Mistral 7B | 7B | 5.7 GB needed | 7.7 GB |
| Qwen2.5 0.5B / 1.5B / 3B / 7B | 7B | 5.1 GB needed | 7.1 GB |
| OLMo 2 1B / 7B | 7B | 5.1 GB needed | 7.1 GB |
| Falcon 3 1B / 3B / 7B | 7B | 5.1 GB needed | 7.1 GB |
| Command R7B | 7B | 5.1 GB needed | 7.1 GB |
| OpenHermes 2.5 | 7B | 5.1 GB needed | 7.1 GB |
How to read this
The AMD RX 550X graphics card features 4 GB of GDDR5 memory. This dedicated memory size determines which artificial intelligence models can run directly on your hardware. To run a model entirely on the graphics processor, the model files and active memory must fit within this 4 GB limit. If a model exceeds this capacity, your system must use alternative execution methods.
Quantization is a compression method that reduces model size. The quant column shows the best available version for each model on this hardware. For example, Q4_K_M and Q5_K_M represent medium compression levels, while Q6_K and Q8_0 represent lighter compression with higher quality. Higher quantization levels require more memory but preserve more of the original model accuracy.
Several capable models fit completely within the 4 GB memory limit of your card. Lumina-Next or Lumina-Image 2.0 at 5B size fits using the Q4_K_M quant which uses 3.7 GB. DeepSeek-VL2 at 4.5B size runs on the Q5_K_M quant using 3.8 GB. For text generation, Gemma 3 4B and Qwen3 4B fit using the Q6_K quant, requiring 3.9 GB of memory. Smaller models like SmolLM3 3B can run at the high quality Q8_0 quant using 3.8 GB.
When a model is too large for the 4 GB graphics memory, you can use CPU offload. This method splits the workload between your graphics card and your system RAM. We assume your computer has 32 GB of system RAM for these setups. For instance, Mistral 7B requires 5.7 GB of memory at the Q4_K_M quant, which uses 7.7 GB of system RAM. Qwen2.5 7B and Falcon 3 7B need 5.1 GB at Q4_K_M, which uses 7.1 GB of system RAM.
CPU offload allows you to run larger models like Stable Diffusion XL, which needs 4.1 GB at FP8 and uses 6.1 GB of system RAM. However, offloading data between the graphics card and system memory slows down processing speeds. You must also consider the 4k context caveat. Running models with long text histories increases memory usage, which can push a fitting model over the 4 GB limit during long conversations.