MI250X vs T4

The MI250X's 383 TFLOPS FP16 and FP32 throughput vastly outpaces the T4's 8.1 TFLOPS, translating to roughly 47 times faster compute for AI training and inference tasks. Equal FP16 and FP32 rates on MI250X indicate balanced performance across precisions, ideal for mixed workloads without bottlenecks in single-precision tasks common in model training. T4's lower figures limit it to smaller models or lighter inference.

Memory specs define real-world viability: MI250X's 128 GB HBM2e supports batch sizes up to eight times larger than T4's 16 GB GDDR6, crucial for training large language models without splitting across GPUs. The 3277 GB/s bandwidth on MI250X, over 10 times the T4's 320 GB/s, accelerates data movement and reduces latency in memory-bound operations like matrix multiplications.

Power draw impacts deployment: MI250X's 560W TDP demands robust cooling and infrastructure, while T4's 70W enables dense server packing for cost-effective inference at scale.

Select the MI250X for workloads demanding extreme scale, such as training large language models requiring 128 GB VRAM to fit entire datasets. Its 3277 GB/s bandwidth handles massive batch sizes efficiently, outperforming T4's 320 GB/s by over 10 times in memory-intensive scientific computing or simulations. Cloud pricing at $1.28 per hour average makes it viable for high-throughput production environments.

Opt for the T4 in power-constrained or budget setups, where 70W TDP allows high-density deployments without excessive cooling costs. It excels in lightweight inference on models fitting within 16 GB GDDR6, with starting cloud pricing of $0.53 per hour suiting development or edge computing. Lower 8.1 TFLOPS performance suffices for non-critical tasks avoiding MI250X's 560W overhead.