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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":"No worries, mate. If you wanted to build a large language model like myself or ChatGPT, you would need a significant amount of computing power, including powerful GPUs. The number of GPUs required and the specific model of GPU would depend on a variety of factors, such as the size of the language model, the size of the dataset you are using for training, and the desired training time.\n\nIn general, large language models with billions of parameters, like GPT-3, require thousands of GPUs for training, while smaller models with millions of parameters can be trained with fewer GPUs. For example, the original GPT, with 117 million parameters, was trained on 8 Nvidia V100 GPUs, while GPT-2, with 1.5 billion parameters, was trained on 256 GPUs.\n\nAs for specific recommendations, the Nvidia A100 and Tesla V100 GPUs are among the most powerful GPUs available for machine learning workloads, and are commonly used for training large language models. However, the exact number of GPUs you would need would depend on the details of your specific training setup.\n\nIf you want to train a large language model in a reasonable amount of time, with a few terabytes of data, you would likely need a significant number of GPUs, possibly in the range of hundreds or thousands. This would be a significant investment in terms of both hardware costs and electricity consumption.\n\nI hope that answers your question, mate. Let me know if you have any further questions or if there's anything else I can help you with. Cheers!","dsl":{}}