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2min chapter
Self-hosting & scaling models (Practical AI #243)
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Challenges of incorporating large language models in an air-gapped edge environment
This chapter explores the difficulties of utilizing big language models in an air-gapped edge environment and suggests the potential solution of setting up an API and connecting it externally instead of running the models locally. They also discuss the option of running Kubernetes on one's own infrastructure or using model hosting services, sharing a customer's anecdote about purchasing GPUs for Kubernetes deployment.
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