This poses significant hurdles for live deployments. Since LLMs are predominantly memory-limited during operation, serving numerous users concurrently is restricted by GPU memory capacity rather than processing power. "Efficient KV cache handling is essential, as inactive caches must be rapidly moved from GPU memory to free space for other sessions, and promptly reloaded when conversations resume," explained Adrian Lancucki, Senior Deep Learning Engineer at Nvidia, to VentureBeat. "These operational expenses are increasingly appearing in commercial offerings (e.g., 'prompt caching') with extra fees for storage services."
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