GB300 vs RTX 2080

The GB300's FP16 performance of 2250 TFLOPS vastly exceeds the RTX 2080's 10.1 TFLOPS, enabling over 222 times faster half-precision computations critical for deep learning training. Its FP32 throughput of 90 TFLOPS, compared to 10.1 TFLOPS, accelerates single-precision scientific simulations by nearly ninefold. This disparity means GB300 handles large-scale model training where RTX 2080 fails due to insufficient tensor core efficiency.

Memory specifications define real-world usability: GB300's 288 GB HBM3e allows batch sizes for models exceeding hundreds of billions of parameters, while RTX 2080's 8-11 GB GDDR6 restricts it to small models or low-batch inference. The 12000 GB/s bandwidth on GB300 supports rapid data transfers, reducing bottlenecks in memory-bound tasks, versus 616 GB/s on RTX 2080 which limits throughput for high-resolution workloads.

Power and form factor differences impact deployment: GB300's 1400W TDP and SXM form with NVSwitch/NVLink suit clustered datacenter inference, offering scalability absent in RTX 2080's 215W PCIe design with basic NVLink.

The GB300 excels in enterprise AI environments requiring massive scale. Its 288 GB VRAM and 2250 TFLOPS FP16 performance make it ideal for training large language models or conducting high-throughput inference on clusters. Users with NVSwitch interconnect needs benefit from its datacenter optimization, despite no current live cloud offers.

Scenarios involving FP8 workloads at 4500 TFLOPS or 12000 GB/s bandwidth for enormous datasets favor GB300 over legacy options.

The RTX 2080 suits budget-conscious users for entry-level tasks. With cloud pricing from $0.05 per hour, it handles light gaming, basic inference, or prototyping on 8-11 GB VRAM without high power demands of 215W TDP.

It provides value for non-AI compute or Stable Diffusion generation where 10.1 TFLOPS FP16 suffices and PCIe form factor enables easy local deployment.