When it comes to choosing a graphics card for running AI, there are a few factors to consider such as the size of the dataset, the complexity of the model, and the level of performance required. Here are some recommendations and key insights from various sources:

- According to Solutions Review, some of the best GPUs for deep learning and data science include NVIDIA GeForce RTX 3090, NVIDIA A100, and AMD Radeon RX 6800 XT. These GPUs have high performance and are suitable for running complex AI models. However, they may be expensive and may not be necessary for less demanding use cases.

- Tim Dettmers, a deep learning researcher, has created a recommendation flow-chart to help users select the most cost-effective GPU that has enough memory for their use-case. He also suggests using cloud GPUs like vast.ai or Lambda Cloud if the GPU is needed sporadically. However, cloud GPUs may not be the best option for long-term and high-usage needs.

- Puget Systems recommends using Intel Xeon W or AMD Threadripper Pro CPUs for AI workloads as they offer excellent reliability and memory performance. For GPUs, they recommend NVIDIA GeForce RTX 3080, 3080 Ti, and 3090 for excellent performance, but for configurations with three or four GPUs, the “pro” series RTX A5000 and high-memory A6000 are recommended. 

- Run.ai suggests considering data parallelism, memory use, and the performance of the GPU when scaling up an algorithm across multiple GPUs. They recommend using data center grade GPUs and high-end deep learning systems like NVIDIA’s DGX series for large-scale deep learning projects.

In summary, the best consumer graphics card for running AI depends on the specific needs of the user. For less demanding use cases, a lower-end GPU may be sufficient, while for large-scale and complex AI models, a high-end GPU may be necessary. Users should consider factors such as performance, cost, and memory requirements when selecting a GPU.
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