GPU vs CPU Execution Tradeoffs

Concerns about pushing tool execution into GPU context where I/O unpredictability and blocking calls cause latency issues, versus cheaper CPU execution.

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.

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very cool idea. But, time savings are not true for every tool call, and it's not clear to me yet whether this is batch-able; also, intuitively, for most of the models that run on GPU, you'd still want to offload tool exec part to CPU since it's much cheaper...

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If you push tool execution into the model itself, you inherit all the I/O unpredictability and error handling baggage, but now inside a GPU context that's allergic to latency. Inference throughput tanks if external calls start blocking, and A100s make expensive waiters. Batching is fantasy unless you know up front exactly what gets executed, which is the opposite of dynamic tools. If you want "faster" here, the trade is reliable deterministic compute versus the usual Wild West of system calls and side effects.

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big question is how efficient is this compare to executing assembly on CPU

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