Businesses opt for self-hosting LLMs primarily due to privacy and security concerns, performance enhancement, and long-term cost benefits associated with large-scale applications.
Self-hosting LLMs is a complex endeavor because of challenges like model size limitations, the need for expensive GPUs, and the fast pace of advancements in the field.
To combat model size issues, quantization is crucial; using larger models reduced to 4-bit often yields better performance than full-precision alternatives.
Optimizing inference through techniques like batching and parallelism can further enhance GPU efficiency, making self-hosting more viable despite inherent challenges.
#self-hosting #large-language-models #performance-optimization #cost-efficiency #privacy-and-security
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