Best local AI models for AMD R9 380
2 GB GDDR5. At a 4k context, 56 of the 233 models in our catalog with verified parameter counts fit fully, up to Allegro at 2.8B parameters.
Check your own machine against every model →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.
| Model | Parameters | Best quant that fits | Memory used at 4k |
|---|---|---|---|
| Allegro | 2.8B | Q4_K_M | 2 GB |
| Open-Sora Plan | 2.7B | Q4_K_M | 2 GB |
| LFM2 1.2B / 2.6B | 2.6B | Q4_K_M | 1.9 GB |
| Playground v2.5 | 2.6B | Q4_K_M | 1.9 GB |
| Stable Diffusion 3.5 Medium | 2.5B | Q4_K_M | 1.8 GB |
| Canary 1B / Qwen-2.5B | 2.5B | Q4_K_M | 1.8 GB |
| SeamlessM4T v2 | 2.3B | Q5_K_M | 2 GB |
| Parler-TTS | 2.2B | Q5_K_M | 1.9 GB |
| Kimi K3 DSpark | 2.2B | Q5_K_M | 2 GB |
| SmolVLM 256M / 500M / 2B | 2B | Q6_K | 2 GB |
| Stable Diffusion 3 Medium | 2B | Q6_K | 2 GB |
| Pyramid Flow | 2B | Q6_K | 2 GB |
| Wav2Vec2 / XLS-R | 2B | Q6_K | 2 GB |
| Moondream 2 | 1.9B | Q6_K | 1.9 GB |
| Qwen3 1.7B | 1.7B | Q6_K | 1.7 GB |
| SmolLM2 135M / 360M / 1.7B | 1.7B | Q6_K | 1.7 GB |
| StableLM 2 1.6B | 1.6B | Q8_0 | 2 GB |
| Sana 0.6B / 1.6B | 1.6B | Q8_0 | 2 GB |
| Zonos 0.1 | 1.6B | Q8_0 | 2 GB |
| Dia 1.6B | 1.6B | Q8_0 | 2 GB |
| Whisper Large v3 | 1.55B | Q8_0 | 2 GB |
| ControlNet / T2I-Adapter / IP-Adapter | 1.5B | Q8_0 | 1.9 GB |
| Hunyuan-DiT | 1.5B | Q8_0 | 1.9 GB |
| Stable Video Diffusion | 1.5B | Q8_0 | 1.9 GB |
| Whisper Large v2 / turbo | 1.5B | Q8_0 | 1.9 GB |
| AudioGen | 1.5B | Q8_0 | 1.9 GB |
| AudioLDM 2 | 1.5B | Q8_0 | 1.9 GB |
| Tango 2 | 1.4B | Q8_0 | 1.8 GB |
| TinyLlama 1.1B | 1.1B | Q8_0 | 1.4 GB |
| SantaCoder 1.1B | 1.1B | Q8_0 | 1.4 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 Q4_K_M | System RAM at 4k |
|---|---|---|---|
| SmolLM3 3B | 3B | 2.2 GB needed | 4.2 GB |
| Replit Code v1.5 3B | 3B | 2.2 GB needed | 4.2 GB |
| Kandinsky 3.1 | 3B | 2.2 GB needed | 4.2 GB |
| Voxtral Mini / Small | 3B | 2.2 GB needed | 4.2 GB |
| Orpheus TTS | 3B | 2.2 GB needed | 4.2 GB |
| Higgs Audio v2 | 3B | 2.2 GB needed | 4.2 GB |
| MusicGen small/medium/large | 3.3B | 2.4 GB needed | 4.4 GB |
| Stable Diffusion XL | 3.417B | 4.1 GB needed | 6.1 GB |
| SDXL Turbo | 3.5B | 2.6 GB needed | 4.6 GB |
| SDXL Lightning | 3.5B | 2.6 GB needed | 4.6 GB |
How to read this
The AMD Radeon R9 380 graphics card has 2 GB of GDDR5 memory. This onboard memory size is the main limit for running local AI models. To run a model completely on your graphics card the model files must fit inside this 2 GB space. If a model is too large it will not load or it will run very slowly.
The best quant column shows the optimal quantization level for each model. Quantization reduces the size of AI models by compressing their weights. For example the Allegro 2.8B model fits in 2 GB of memory when compressed to the Q4_K_M quant. Smaller models like the SmolLM2 1.7B can use the higher quality Q6_K quant and still fit within 1.7 GB of memory. TinyLlama 1.1B can run at the high quality Q8_0 quant using only 1.4 GB of memory.
You can run larger models by using CPU offload if your computer has 32 GB of system RAM. CPU offload splits the model between your graphics card and your system memory. This allows you to load models that exceed 2 GB but it reduces your processing speed. For example the SmolLM3 3B model needs 2.2 GB of video memory at the Q4_K_M quant and also requires 4.2 GB of system RAM.
Image generation models also support CPU offload on this hardware. Stable Diffusion XL has 3.417B parameters and needs 4.1 GB of video memory at FP8 or optimized settings along with 6.1 GB of system RAM. SDXL Turbo and SDXL Lightning both have 3.5B parameters and need 2.6 GB of video memory at the Q4_K_M quant plus 4.6 GB of system RAM.
When running local text models you must consider the 4k context limit. Running a model with a larger context window increases the memory used by the system. If you increase the context length beyond the standard limit the model will exceed the 2 GB memory of your R9 380 and cause slowdowns.