MI300X vs RTX 3080

The MI300X's FP16 performance reaches 1307 TFLOPS, over 43 times the RTX 3080's 29.8 TFLOPS, accelerating deep learning training where half-precision dominates. This delta translates to faster convergence on large datasets, reducing epochs from days to hours on the MI300X. FP32 at 163 TFLOPS on the MI300X still surpasses the RTX 3080's 29.8 TFLOPS, benefiting simulations requiring full precision.

Memory capacity defines workload feasibility: 192 GB HBM3 on the MI300X supports massive batch sizes for models exceeding 70 billion parameters, while 10 to 12 GB GDDR6X on the RTX 3080 restricts users to small batches or model sharding. Bandwidth at 5300 GB/s versus 760 GB/s minimizes bottlenecks in data-intensive inference, enabling higher throughput without stalling. The MI300X's FP8 at 2614 TFLOPS further optimizes low-precision inference, cutting latency by orders of magnitude over the RTX 3080.

Power draw reflects efficiency trade-offs: the MI300X's 750W TDP suits dense clusters, while the RTX 3080's 320W fits edge or desktop use, though at reduced scale.

Opt for the MI300X in large-scale AI training or inference where VRAM exceeds 12 GB, such as processing 175 billion parameter models: its 192 GB HBM3 handles full context without offloading. High-bandwidth tasks benefit from 5300 GB/s, supporting batch sizes up to 512 versus the RTX 3080's limits around 8 to 16. Datacenter deployments leverage Infinity Fabric and PCIe 5.0 for multi-GPU scaling at 1307 TFLOPS FP16.

Select the RTX 3080 for budget-conscious prototyping or gaming-integrated workflows: its $0.06 per hour starting price enables experimentation at 1/8th the MI300X's $0.50 minimum. Small models under 7 billion parameters fit within 10 to 12 GB GDDR6X, with 29.8 TFLOPS FP16 sufficing for fine-tuning or Stable Diffusion at low cost. Lower 320W TDP reduces cooling needs in personal or small cloud instances.