MI300X vs RTX 2080

The MI300X's 192 GB HBM3 VRAM dwarfs the RTX 2080's 8-11 GB GDDR6, allowing batch sizes thousands of times larger in training and inference tasks. This VRAM advantage supports models exceeding 100 billion parameters on the MI300X, while the RTX 2080 struggles with even modest large language models due to out-of-memory errors.

Memory bandwidth tells a similar story: 5300 GB/s on the MI300X versus 616 GB/s on the RTX 2080 accelerates data movement in memory-bound workloads like transformer training, reducing epoch times dramatically. FP16 performance of 1307 TFLOPS on the MI300X enables rapid mixed-precision training, where the RTX 2080's 10.1 TFLOPS limits throughput to legacy scales; FP32 at 163 TFLOPS versus 10.1 TFLOPS favors the MI300X for scientific simulations requiring single-precision accuracy.

The FP16 to FP32 ratio highlights specialization: MI300X's 8:1 delta optimizes AI inference with FP8 at 2614 TFLOPS, while RTX 2080's parity suits graphics but bottlenecks deep learning. Higher 750W TDP on MI300X demands robust cooling, contrasting the efficient 215W of RTX 2080 for edge deployments.

Opt for the MI300X in large-scale AI training or inference where 192 GB VRAM and 1307 TFLOPS FP16 performance handle billion-parameter models without multi-GPU complexity. Data center tasks like scientific computing benefit from 5300 GB/s bandwidth and Infinity Fabric interconnects for clustered scaling.

High-throughput environments justify its $2.63 per hour average cost when deadlines demand the 130x FP16 uplift over RTX 2080.

Choose the RTX 2080 for cost-sensitive prototyping or gaming at $0.09 per hour average, where 8-11 GB VRAM suffices for small models or Stable Diffusion image generation. Light inference on models under 7 billion parameters leverages its 10.1 TFLOPS FP16 without overprovisioning.

Edge or desktop setups favor its 215W TDP and PCIe form factor for quick setups without data center infrastructure.