rust-gpu-scheduling

VRAM-aware GPU scheduling prevents out of memory errors by applying per-model memory budgets and safety margins across multiple ML pipelines. It tracks loaded and unloaded model states to cap concurrent workloads within your available memory.

GPU scheduling for ML workloads handles task dependencies and priority queues by tracking work item execution order and priority levels. Dependency tracking enforces correct sequential execution while priority queuing reorders tasks dynamically as VRAM resources change.

Managing model lifecycle state reduces GPU reload times by tracking loaded and unloaded states alongside estimated load durations. This lifecycle management minimizes costly model reloads during multi-model pipeline orchestration by maintaining active state awareness.

VRAM budgeting caps concurrent workloads across models like Whisper and Qwen3 using per-model memory accounting and semaphore-based limiting. This approach governs task execution by enforcing strict memory limits tailored to each specific model's requirements.

GPU scheduling supports reordering ML tasks when VRAM resources change dynamically by applying queue-based scheduling with dependency tracking. This dynamic reordering ensures workload execution continues efficiently without exceeding memory budgets during resource fluctuations.

Semaphore-based limiting for GPU memory management relies on simplified concurrency capping rather than precise real-time VRAM allocation tracking. It governs task execution queues effectively but may require safety margins to account for allocation overhead during dynamic model loading.