Multimodal Inference Capacity and GPU Cluster Provisioning Simulation Script

Role: Principal MLOps Data Scientist with 12+ years optimizing high-throughput distributed diffusion inference clusters.

Context

• Cluster Infrastructure: {{inference_cluster_config}}
• Historical Ingestion Metrics: {{historical_prompt_traffic_log}}
• Sampling Profiles: {{diffusion_step_parameters}}
• Target Thresholds: {{peak_sla_latency_target}}
• Forecasting Window: {{forecast_horizon_weeks}}
• Demand Trajectory: {{target_concurrency_growth_rate}}

Task

Develop an enterprise-grade Python predictive modeling and discrete-event simulation script that forecasts GPU node exhaustion, cold-start queuing bottlenecks, and dynamic autoscaling thresholds for text-to-image and multimodal generation endpoints over {{forecast_horizon_weeks}}.

Method

1. Ingest {{historical_prompt_traffic_log}} to fit a non-homogeneous Poisson process capturing prompt arrival spikes and diurnal seasonality.
2. Parameterize GPU memory allocation, KV-cache behavior, and step execution time across hardware nodes defined in {{inference_cluster_config}}.
3. Model prompt complexity variances (aspect ratios, batch sizes, guidance scales) using the distribution in {{diffusion_step_parameters}}.
4. Apply compound growth over {{forecast_horizon_weeks}} based on {{target_concurrency_growth_rate}}.
5. Run a Monte Carlo discrete-event simulation tracking queue formation, GPU VRAM fragmentation, and TTFT (time-to-first-token/tile).
6. Compute SLA violation likelihood against {{peak_sla_latency_target}} across P50, P95, and P99 intervals.
7. Generate dynamic autoscaler rules providing optimal scale-out triggers that minimize under-provisioned dropouts and idle compute costs.
8. Export summary capacity tables, confidence intervals, and JSON configuration payloads for orchestrator deployment.

Constraints

• Code MUST be written in executable, modular Python 3.11+ using standard scientific libraries (NumPy, SciPy, SimPy, Pandas).
• The script MUST output parameterized CLI arguments and include structured docstrings for all mathematical formulations.
• MUST NOT use stubbed mock algorithms or truncated placeholder functions.
• Hardcode fallback thresholds if empirical distributions fail convergence checks.

Output format

Executable Python script organized into:

1. Configuration and Data Ingestion Classes
2. Statistical Arrival and Workload Engine
3. Discrete-Event Simulation Pipeline
4. Capacity Reporting & SLA Violation Forecaster
5. CLI Interface and Main Execution Block (150-250 lines total code structure)

Self-review

• Confirm that all formulas account for variable denoising step latencies in {{diffusion_step_parameters}}.
• Verify that the simulation monitors both GPU compute saturation and VRAM ceiling limits from {{inference_cluster_config}}.
• Check that P95/P99 latency calculations strictly reflect the constraints in {{peak_sla_latency_target}}.