Cheapest Cloud GPUs: Where AI Teams Save on Compute

The recent surge in demand for generative AI and large language models has led to a spike in GPU prices, prompting the emergence of alternative GPU cloud providers and multi-cloud strategies. Small teams and startups can navigate this landscape by identifying cost-effective options like Northflank, Thunder Compute, and RunPod, which offer affordable A100 and H100 rentals. It's essential to consider hidden costs such as data egress, storage, and idle time, while strategies like using a mix of on-demand, spot, and Bring-Your-Own-Compute (BYOC) can help balance cost, availability, and control. Clarifai's compute orchestration layer offers a solution by managing heterogeneous hardware across multiple clouds, reducing costs through automatic resource selection and batching. Emerging hardware like NVIDIA's H200 and B200, and AMD's MI300X, offer increased memory and bandwidth, potentially altering price-performance dynamics. Ultimately, the key to optimizing GPU rental costs lies in adopting a multi-provider strategy, leveraging serverless models, and maximizing GPU utilization through careful workload management and batching.

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