MI355X vs RTX 5080

The MI355X vastly outpaces the RTX 5080 in raw compute: 2300 TFLOPS FP16 and FP32 versus 56.3 TFLOPS each, translating to over 40 times faster matrix operations for AI training. This delta means training large language models completes in hours on MI355X clusters rather than days on RTX 5080 setups. FP8 at 4600 TFLOPS on MI355X accelerates quantized inference, reducing latency for real-time applications.

Memory specs amplify these advantages: 288 GB HBM3e on MI355X supports batch sizes up to thousands for models exceeding 100 billion parameters, while 16 GB GDDR7 on RTX 5080 limits batches to dozens, causing out-of-memory errors in complex simulations. The 8000 GB/s bandwidth of MI355X ensures data flows without bottlenecks during gradient computations, compared to 960 GB/s on RTX 5080, which throttles large-scale inference.

Power efficiency tilts toward RTX 5080 at 360W TDP, achieving 0.156 TFLOPS per watt in FP16, versus MI355X's 3.07 TFLOPS per watt at 750W. For inference-heavy tasks, RTX 5080 handles smaller payloads efficiently, but MI355X dominates sustained high-throughput workloads.

The MI355X excels in enterprise AI training and scientific computing requiring immense scale. With 288 GB HBM3e VRAM and 2300 TFLOPS FP16, it processes trillion-parameter models without multi-GPU sharding, ideal for research labs handling petabyte datasets via 8000 GB/s bandwidth. Infinity Fabric interconnects enable seamless multi-node scaling for simulations exceeding single-GPU limits.

Opt for the RTX 5080 in cost-sensitive, single-user scenarios like gaming or small-scale inference. Its 16 GB GDDR7 and 360W TDP fit PCIe form factors for desktop setups, with cloud pricing from $0.25 per hour making prototyping accessible. At 56.3 TFLOPS FP16, it suffices for fine-tuning models under 10 billion parameters without datacenter overhead.