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What are good consumer graphics cards for running AI?

{"text":"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:\n\n- 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.\n\n- 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.\n\n- 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 \u201cpro\u201d series RTX A5000 and high-memory A6000 are recommended. \n\n- 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\u2019s DGX series for large-scale deep learning projects.\n\nIn 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.","dsl":{}}

Thank you for the summary. If I wanted to build a large language model such as yourself or chatGPT, how many GPUs would be needed and can you recommend any from the suggestions you gave above? Assuming you gave a recommendation of the type of GPU, how many would be needed to train a large language model in a reasonable amount of time (several days for example) on a large dataset consisting of a few terrabytes of data.

{"text":"Training a large language model such as ChatGPT requires a significant amount of computing power, and the number of GPUs required can vary depending on the specific model architecture and dataset size.\n\nFor example, the largest version of GPT-3, which has 175 billion parameters, was trained on a cluster of thousands of GPUs. However, smaller models with fewer parameters could be trained on a single high-end GPU or a smaller cluster.\n\nIn terms of specific recommendations, some of the GPUs mentioned earlier, such as NVIDIA A100 or GeForce RTX 3090, would be suitable for training large language models. However, it's worth noting that these GPUs can be expensive and may not be necessary for smaller models or datasets.\n\nTo train a large language model in a reasonable amount of time, you would likely need a cluster of GPUs, with the exact number depending on the specific use case. For example, OpenAI's GPT-3 used a cluster of thousands of GPUs to train their largest model. However, smaller models could be trained on a smaller cluster of GPUs or even a single high-end GPU.\n\nIn summary, training a large language model requires a significant amount of computing power, and the number of GPUs needed can vary depending on the specific model and dataset size. Some of the GPUs recommended earlier, such as NVIDIA A100 or GeForce RTX 3090, would be suitable for training large language models, but the exact number of GPUs needed would depend on the specific use case.","dsl":{}}