Best local AI models for NVIDIA Quadro 410
0.5 GB DDR3. At a 4k context, 12 of the 233 models in our catalog with verified parameter counts fit fully, up to Qwen3 0.6B at 0.6B parameters.
Check your own machine against every model →The largest models that fit fully
The 12 largest of the 12 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 |
|---|---|---|---|
| Qwen3 0.6B | 0.6B | Q5_K_M | 0.5 GB |
| PixArt-α / PixArt-Σ | 0.6B | Q5_K_M | 0.5 GB |
| Parakeet TDT 0.6B v2 | 0.6B | Q5_K_M | 0.5 GB |
| XTTS v2 | 0.5B | Q6_K | 0.5 GB |
| Spark-TTS | 0.5B | Q6_K | 0.5 GB |
| CosyVoice 2 | 0.5B | Q6_K | 0.5 GB |
| VALL-E X (unofficial) | 0.4B | Q8_0 | 0.5 GB |
| ERNIE 4.5 open weights | 0.3B | Q8_0 | 0.4 GB |
| F5-TTS | 0.3B | Q8_0 | 0.4 GB |
| E2-TTS | 0.3B | Q8_0 | 0.4 GB |
| ChatTTS | 0.3B | Q8_0 | 0.4 GB |
| StyleTTS 2 | 0.15B | FP16 | 0.4 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 |
|---|---|---|---|
| Florence-2 base/large | 0.77B | 0.6 GB needed | 2.6 GB |
| Magenta RT | 0.8B | 0.6 GB needed | 2.6 GB |
| Stable Diffusion 2.1 | 0.9B | 0.7 GB needed | 2.7 GB |
| Bark | 0.9B | 0.7 GB needed | 2.7 GB |
| Tortoise TTS | 0.9B | 0.7 GB needed | 2.7 GB |
| Riffusion (SD-based) | 0.9B | 0.7 GB needed | 2.7 GB |
| Gemma 3 1B | 1B | 0.7 GB needed | 2.7 GB |
| Llama 3.2 1B / 3B | 1B | 0.7 GB needed | 2.7 GB |
| MMS (1100+ languages) | 1B | 0.7 GB needed | 2.7 GB |
| CSM-1B | 1B | 0.7 GB needed | 2.7 GB |
How to read this
The NVIDIA Quadro 410 is an entry level legacy graphics card equipped with 0.5 GB of DDR3 memory. This extremely small onboard memory pool severely limits the size of artificial intelligence models you can run directly on the hardware. To run local models successfully, you must target highly optimized architectures and use aggressive quantization to fit the model weights into the available space.
For models that fit entirely on the card, the memory size dictates the maximum parameter count you can load. The quantization column shows the compression format needed to reduce the model size. For example, Qwen3 0.6B, PixArt-α, PixArt-Σ, and Parakeet TDT 0.6B v2 can run using the Q5_K_M quant which uses exactly 0.5 GB of memory. Models like XTTS v2, Spark-TTS, and CosyVoice 2 are 0.5B models that require a Q6_K quant to fit within the same 0.5 GB limit.
Even smaller models can run with less compression because they have fewer parameters. VALL-E X (unofficial) is a 0.4B model that fits using a Q8_0 quant at 0.5 GB used. ERNIE 4.5 open weights, F5-TTS, E2-TTS, and ChatTTS are 0.3B models that use 0.4 GB of memory at a Q8_0 quant. The smallest model in this tier is StyleTTS 2 which is a 0.15B model that can run at FP16 quality while using 0.4 GB of memory.
If you want to run larger models, you must use CPU offload. This process splits the model layers between your graphics card and your system memory. Assuming a 32 GB system RAM setup, you can run Florence-2 base/large or Magenta RT. These models require 0.6 GB of graphics memory at Q4_K_M quant and 2.6 GB of system RAM.
Other offload options include Stable Diffusion 2.1, Bark, Tortoise TTS, and Riffusion (SD-based). These models require 0.7 GB of graphics memory at Q4_K_M quant and 2.7 GB of system RAM. You can also run Gemma 3 1B, Llama 3.2 1B / 3B, MMS (1100+ languages), and CSM-1B using the same 0.7 GB of graphics memory at Q4_K_M quant and 2.7 GB of system RAM.
Be aware of the 4k context caveat when running these configurations. Generating longer text sequences requires additional memory for the context window. On a card with only 0.5 GB of memory, expanding the context window to 4k tokens will likely exceed your hardware limits and cause out of memory errors.