llm/3fd5f01c-dce0-45f5-821d-a9c655fbe87c/topic-9-d615e8b2-7d8d-414a-967d-7f41ff063897-output.json
While moving tool execution into a GPU context is a novel concept, critics argue it introduces significant risks regarding latency and cost-efficiency. Using expensive hardware like A100s to handle unpredictable I/O or blocking system calls turns high-performance processors into "expensive waiters," potentially tanking inference throughput. Furthermore, because dynamic tool calls are difficult to batch, many experts suggest it is far more practical to offload these tasks to cheaper CPUs where assembly-level execution remains highly efficient. Ultimately, the trade-off involves sacrificing reliable, deterministic compute for a "Wild West" of side effects and external dependencies.