When scaling multi-turn conversation systems to thousands of concurrent requests, GPU memory utilization rarely matches theoretical expectations. While tensor memory remains static based on model parameters, the key-value cache expands dynamically with sequence length. Unclaimed tensor allocations during aborted client connections frequently trigger memory leaks that drop token throughput significantly before out-of-memory errors register.
Tracing Cache Allocation Under Dynamic Sequence Lengths
Standard CUDA memory allocators do not automatically defragment physical VRAM when request context buffers collapse. During dynamic batching, early sequence termination leaves orphaned pages allocated inside the sequence cache pool. Profiling with custom PyTorch memory hooks reveals that active attention calculations often lock non-contiguous pages, preventing the allocator from reclaiming multi-gigabyte blocks.
Implementing Zero-Copy Paged Memory Management
Transitioning from contiguous memory block allocations to paged virtual tables isolates key and value state into fixed-size physical blocks. By mapping logical sequence indices to arbitrary non-contiguous physical pages, physical memory reuse approaches near optimal efficiency. This pattern eliminates internal fragmentation entirely and stabilizes per-request inference latency under bursty production workloads.
Production Metrics to Monitor Cache Efficiency
Monitoring total VRAM usage alone provides inadequate visibility into cache exhaustion risks. Production clusters must track active page fragmentation ratio alongside effective token throughput per watt. Alert thresholds based on non-contiguous page free space allow automated batch rescheduling before active client connections experience forced context truncation.