Maximizing AI Performance: Harnessing Multiple GPUs with Beam Cloud
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In this riveting episode by NeuralNine, the team delves into the exhilarating world of maximizing AI performance by harnessing the power of multiple GPUs. Picture this: you're faced with a colossal AI model that demands more vRAM than your average GPU can handle. What do you do? The answer lies in combining the might of two smaller GPUs to conquer the task at hand. It's a symphony of technology and ingenuity, pushing the boundaries of what's possible in the realm of artificial intelligence.
Enter the stage, the formidable stable diffusion XL, a model that commands respect with its voracious appetite for vRAM. The team takes us on a thrilling coding adventure in Python, showcasing the process of loading and utilizing this powerhouse locally. But the real magic unfolds when they transport this wizardry to a serverless endpoint, where the true test begins. Can a single GPU stand tall against the vRAM behemoth, or will the team need to call upon the dynamic duo of two GPUs to save the day?
Beam Cloud emerges as the unsung hero, offering a platform where dreams of GPU acceleration become reality. With free credits in hand, the team embarks on a journey to deploy their code on a serverless endpoint with access to multiple GPUs. The adrenaline is palpable as they configure the Beam client, set up the API token, and define the GPU endpoint with precision. It's a high-octane race against time as they navigate the intricacies of GPU utilization, measuring peak memory usage, and unleashing the full potential of their AI models.
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Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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