Best local AI models for AMD R9 M360
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 R9 M360 graphics card features 4 GB GDDR5 memory. This memory size determines which local AI models can run entirely on your hardware. To run a model without slowdowns, the model files and active memory must fit within this 4 GB limit. If a model exceeds this capacity, your system must use slower system memory.
The quantization column indicates the compression level applied to each model. Quantization reduces the size of the model weights to save space. For example, a Q4_K_M quant uses less memory than a Q6_K or Q8_0 quant. Higher quant numbers preserve more original model quality but require more of your 4 GB video memory.
Several capable models fit entirely within your hardware limits. Lumina-Next or Lumina-Image 2.0 at 5B with a Q4_K_M quant uses 3.7 GB. DeepSeek-VL2 at 4.5B with a Q5_K_M quant uses 3.8 GB. For text tasks, Phi-4-mini-instruct at 3.8B with a Q6_K quant uses 3.7 GB. You can also run image generators like SDXL Turbo at 3.5B using a Q6_K quant which requires 3.4 GB.
When a model is too large for the 4 GB video memory, you can use CPU offload. This process splits the model between your graphics card and your system RAM. CPU offload allows you to run larger models but it reduces processing speed. These calculations assume your computer has 32 GB of system RAM.
With CPU offload, you can run Mistral 7B using a Q4_K_M quant which needs 5.1 GB of video memory and 7.1 GB of system RAM. Qwen2.5 7B also runs with a Q4_K_M quant using 5.1 GB of video memory and 7.1 GB of system RAM. Stable Diffusion XL at 3.417B needs 4.1 GB at FP8 or optimized settings along with 6.1 GB of system RAM.
You must consider the 4k context caveat when planning your memory usage. Running models with long text histories increases memory consumption. The listed memory requirements cover the base model files. Generating long responses or processing large documents will require additional memory beyond these baseline figures.