Best local AI models for NVIDIA RTX A500 Laptop
4 GB GDDR6. 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 NVIDIA RTX A500 Laptop GPU features 4 GB of GDDR6 memory. This dedicated video memory determines which artificial intelligence models can run entirely on your graphics hardware. Running a model fully on your GPU ensures the fastest processing speeds for text generation, image creation, and audio synthesis.
To fit within the 4 GB limit, models must use quantization. The quantization column shows the compression level used to reduce model size. For example, Q4_K_M, Q5_K_M, Q6_K, and Q8_0 represent different levels of precision. A Q4_K_M quant uses less memory than a Q8_0 quant but sacrifices a small amount of output quality to fit the hardware.
The largest models that fit completely in your GPU memory include Lumina-Next or Lumina-Image 2.0 at 5B parameters using a Q4_K_M quant which takes 3.7 GB of memory. You can also run DeepSeek-VL2 at 4.5B parameters using a Q5_K_M quant which takes 3.8 GB of memory. For standard text tasks, Qwen3 4B and Gemma 3 4B fit well using a Q6_K quant which takes 3.9 GB of memory.
For image generation, you can run SDXL Turbo or SDXL Lightning at 3.5B parameters using a Q6_K quant which takes 3.4 GB of memory. Audio models like MusicGen large at 3.3B parameters fit using a Q6_K quant which takes 3.2 GB of memory. Smaller models like SmolLM3 3B can run at a higher Q8_0 quant taking 3.8 GB of memory.
When a model exceeds 4 GB of video memory, you must use CPU offload. This process splits the model between your GPU and your system RAM. For these cases, we assume your laptop has 32 GB of system RAM. Offloading allows you to run larger models like Mistral 7B which needs 5.7 GB at Q4_K_M and 7.7 GB of system RAM, or Qwen2.5 7B which needs 5.1 GB at Q4_K_M and 7.1 GB of system RAM. Offloading makes these models run much slower.
Keep in mind that these memory figures are calculated using a standard 4k context window. If you increase the context window to process longer documents, the memory requirements will rise. This extra memory usage might force you to use a lower quantization level or rely on CPU offloading.