MI355X vs T4

MI355X vastly outpaces T4 in compute throughput: 2300 TFLOPS FP16 and FP32 enable rapid training of large models, where equal tensor core performance supports mixed-precision workflows without bottlenecks. T4's 8.1 TFLOPS limits it to small-scale training or basic inference. MI355X's FP8 at 4600 TFLOPS further accelerates quantized inference for billion-parameter LLMs.

Memory capacity and speed define real-world viability: MI355X's 288 GB HBM3e handles massive batch sizes for models exceeding 16 GB, preventing out-of-memory errors common on T4. The 8000 GB/s bandwidth sustains data flow during peak loads, versus T4's 320 GB/s which throttles large batches.

Power efficiency varies sharply: T4's 70W TDP allows dense server packing, ideal for inference farms, while MI355X's 750W demands advanced cooling but justifies it through 283 times higher FP16 performance.

Select MI355X for high-throughput AI training and large-model inference: 288 GB VRAM accommodates full LLMs without partitioning, and 2300 TFLOPS FP16 halves training times versus older hardware. It suits scientific computing with terabyte-scale datasets, leveraging 8000 GB/s bandwidth for sustained simulations. OAM form factor and Infinity Fabric optimize multi-node clusters.

Deploy it when future-proofing data centers, as CDNA 4 architecture from 2025 supports emerging FP8 workloads at 4600 TFLOPS.

Opt for T4 in budget-conscious, low-power scenarios: pricing starts at $0.53 per hour across six providers, enabling cost-effective inference for models under 16 GB. Its 70W TDP fits edge servers or dense virtualization without high cooling costs.

T4 excels for always-on services like real-time analytics, where 8.1 TFLOPS FP16 suffices and PCIe compatibility simplifies integration into existing infrastructure.