Where to Buy or Rent GPUs for LLM Inference

Inference at scale for self-hosted large language models (LLMs) involves not just powerful models, but also the right hardware, particularly GPUs, to ensure performance, cost-efficiency, and availability. Choosing the best GPU for LLM inference requires considering factors like GPU memory, performance metrics such as memory bandwidth and compute throughput, cost, availability, and ecosystem support. Different sourcing options include hyperscalers, specialized GPU clouds, decentralized GPU marketplaces, and direct purchase, each with its pros and cons. Multi-cloud and cross-region deployments are recommended to handle unpredictable inference traffic, comply with data residency laws, and avoid vendor lock-in while optimizing costs. The Bento Inference Platform offers a unified solution for managing GPUs across different providers, enhancing autoscaling, and maintaining observability, thereby allowing teams to focus on innovation rather than infrastructure.

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