Tree light xgboost slo aware

[Gregory-Pereira]

What type of PR is this?
/kind feature

What this PR does / why we need it:
This gives an option for implementing CPU inferencing for latency predictor in the treelite library for efficiency. Treelite is purpose-built for the efficient deployment of tree-based models, whereas the XGBoost Python library is designed for training and research flexibility. Once a model is trained, Treelite compiles it into optimized, model-specific C/C++ code, eliminating the interpreter overhead, dynamic branching, and general-purpose runtime structures used by XGBoost’s predictor. This ahead-of-time (AOT) compilation allows Treelite to:

• Vectorize and fuse operations for better CPU/GPU utilization.
• Minimize branching overhead via specialized code paths tailored to the exact tree structure.
• Reduce memory access latency through contiguous, cache-friendly data layouts.
• Avoid Python and framework overhead, resulting in lower latency and higher throughput during inference.

In short, XGBoost is optimized for training and experimentation, while Treelite is optimized for lightweight, production-grade inference — making it more performant when serving models at scale.

Which issue(s) this PR fixes:
Fixes #1937

Does this PR introduce a user-facing change?:

This adds deployment options for latency prediction using Treelite

[k8s-ci-robot]

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This pull-request has been approved by: Gregory-Pereira
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