Data Center GPU
A high-performance GPU aimed at enterprise customers and data centers for AI training and inference. Gross margins typically exceed 60%, and under AI compute demand such products displace capacity for consumer-grade products.
Timeline (last 90 days)
UMA's memory bandwidth leads on mobile, but still lags behind the HBM bandwidth of data-center-grade GPUs, which becomes a bottleneck in high-concurrency inference or training scenarios.
The cost of the unified memory architecture is lower memory bandwidth than the dedicated VRAM of high-end discrete GPUs, so throughput is usually below that of data-center GPUs.
The gross margin of data center GPUs is usually above 60%, while the gross margin of consumer flagship graphics cards is often in the 30%–40% range.
All Facts (3)
UMA's memory bandwidth leads on mobile, but still lags behind the HBM bandwidth of data-center-grade GPUs, which becomes a bottleneck in high-concurrency inference or training scenarios.
50%UnverifiedThe cost of the unified memory architecture is lower memory bandwidth than the dedicated VRAM of high-end discrete GPUs, so throughput is usually below that of data-center GPUs.
50%UnverifiedThe gross margin of data center GPUs is usually above 60%, while the gross margin of consumer flagship graphics cards is often in the 30%–40% range.
50%