I am putting together a control-system framework that runs entirely on an Nvidia RTX 3090 and centres on machine-learning workloads. The goal is a single, modular codebase able to orchestrate three specific tasks—image recognition, natural-language processing, and predictive analytics—while squeezing every ounce of performance the 3090’s CUDA cores and Tensor cores can provide. Core requirements • Modular architecture so each task (vision, NLP, forecasting) lives in its own plug-in or service yet can share common utilities such as data pipes, logging and GPU memory management. • Native GPU acceleration using CUDA 11.x (cuDNN, NCCL, TensorRT or comparable optimiser) with fall-backs abstracted cleanly for future upgrades. • Real-time inference endpoint that exposes a lightweight REST or gRPC API for incoming data and returns results with minimum latency. • Clear configuration layer—JSON/YAML or similar—so I can swap models or adjust batch size without code edits. • Robust monitoring hooks (Prometheus preferred) so temperature, VRAM usage and FPS / tokens-per-second can be tracked live. Deliverables 1. Full source code with build scripts or Dockerfile. 2. Documentation: architecture diagram, setup steps, and sample calls for each task. 3. Benchmark report demonstrating image, NLP and forecasting throughput on the RTX 3090 under typical loads. Acceptance criteria • Framework installs from scratch on an Ubuntu 22.04 box with the latest Nvidia drivers. • Each task returns correct sample outputs in under one second per request when batch size ≤ 4. • GPU utilisation averages above 80 % during the provided benchmarks. Feel free to suggest language bindings (Python, C++ or mixed) and any libraries you believe would sharpen performance; I am open so long as deployment stays straightforward.