Dynamic hardware partitioning in GPU architectures refers to the ability to allocate and reallocate GPU resources (such as compute units, memory bandwidth, or execution queues) to different workloads or tasks at runtime. Unlike static partitioning, where resources are fixed at launch, dynamic partitioning enables better utilization and responsiveness by adapting to workload demands. This is particularly useful in heterogeneous environments or multi-tenant systems where workloads may vary significantly in compute or memory intensity. By dynamically reallocating resources, GPUs can reduce contention, improve throughput, and enhance latency-sensitive performance. However, the overhead of partition switching and the complexity of resource management can introduce trade-offs that must be carefully balanced. Additionally, dynamic partitioning can affect the number of threads running concurrently depending on resource usage such as register pressure. For instance, if a workload uses a large number of registers per thread, fewer threads can be resident per streaming multiprocessor, reducing concurrency. Conversely, if register usage is low, more threads can run concurrently. Similarly, large thread blocks may lead to underutilization of resources if they exceed per-block limits, while smaller thread blocks may allow more flexibility and better occupancy under dynamic partitioning constraints.