llm/3fd5f01c-dce0-45f5-821d-a9c655fbe87c/topic-17-b7e2e419-8db4-4696-b490-eb11034b4542-output.json
Integrating tool execution directly into model processing faces significant hurdles, primarily because GPUs are high-cost resources that shouldn't be left idling during unpredictable external I/O or complex error handling. Critics argue that batching becomes a "fantasy" in this context since efficient parallel processing requires knowing execution paths upfront, which contradicts the inherently dynamic nature of tool use. Furthermore, offloading these tasks to the CPU remains more cost-effective, as the "Wild West" of system calls threatens to tank inference throughput by introducing latency into an environment built for deterministic compute. This creates a stark trade-off between the theoretical speed of integrated tools and the practical reality of maintaining reliable system performance.