Inference engine startup included in GPU metric capture

**Describe the bug** Current scripts in the benchmarks/single_node directory (e.g., https://github.com/SemiAnalysisAI/InferenceX/blob/adbaae52ddf2569ddf2e793f5ad0a56f2f2a4d13/benchmarks/single_node/qwen3.5_fp8_mi325x.sh) follow this pattern to establish GPU monitoring for every InferenceX benchmark run: 1) start_gpu_monitor 2) Start LLM inference engine as a background process (e.g. SGLang, vLLM) 3) wait_for_server_ready 4) Run benchmark for the model (key result to report for InferenceX benchmark) 5) stop_gpu_monitor As-is, the GPU energy consumption and other metrics can be skewed by irregularities in LLM inference engine startup (compilation, kernel autotuning, etc) and the 5-second sleeps used while waiting for the server to start (waiting on the /health route). While cross-referencing other logs can identify some timestamps that fall outside of the actual benchmark window, there is no direct correspondence within the CSV itself. **Expected behavior** Monitoring data should only correspond to the benchmark execution window. Alternatively, providing a way to identify which portion of the data corresponds to benchmark execution would also be acceptable. **Potential solutions** Adding a marker in the `gpu_metrics.csv` or delaying the `start_gpu_monitor` calls until the benchmark is ready to run (moving step 1 above to just before step 4) can assist in disambiguating the results. **Additional context** The startup costs of pinned versions of vLLM (likely similar for other engines) should be relatively consistent for a GitHub action runner, but the inference engine's startup performance can change over successive versions. The ability to separate startup costs from the benchmark data is important.