On a CPU, hyperthreads are mostly replicated register banks. This allows the CPU to hold the context for 2 threads simultaneously. And, lets parts of a CPU make progress on one thread while the other thread is stalled. CPUs also has a kinda large microcode register bank that helps work around dependencies in asm instructions that reuse named registers.

On the GPU however, the hyperthreads are just a round-robin execution queue to take advantage of instruction pipelining. The register bank of a single GPU core is huge and can be flexibly divided across a variable number of thread contexts when a kernel is launched. Many thread contexts can be held in registers simultaneously in a single GPU core. That makes stalling on memory latency much less of a problem. The hardware can focus on delivering raw bandwidth with high latency and get great overall performance. This throughput-instead-of-latency trade-off extends to many other aspects of GPU design.