MI325X vs T4

Compute performance defines the core disparity: MI325X achieves 1307 TFLOPS in both FP16 and FP32, enabling swift training of large neural networks where the T4's 8.1 TFLOPS limits scale. This FP16/FP32 parity on MI325X supports balanced mixed-precision training without bottlenecks, unlike older architectures.

For inference, MI325X's FP8 capability at 2614 TFLOPS accelerates low-precision deployments, processing thousands more tokens per second than T4. Memory bandwidth of 6000 GB/s on MI325X sustains large batch sizes in transformer models, reducing latency; T4's 320 GB/s constrains batches, increasing per-inference overhead.

In practice, 256 GB VRAM on MI325X accommodates full-model loading for fine-tuning billion-parameter LLMs, avoiding fragmentation seen with T4's 16 GB. High TDP of 750W reflects this power for sustained peaks, while T4's 70W suits intermittent low-intensity runs.

Opt for the MI325X in memory-intensive AI training where models exceed 16 GB. Its 256 GB HBM3e VRAM and 6000 GB/s bandwidth handle massive datasets without swapping. Infinity Fabric enables efficient multi-GPU clusters for scaling large language model training.

Scientific simulations benefit from 1307 TFLOPS FP32, processing complex computations rapidly.

Choose the T4 for cost-effective inference on small models under 16 GB VRAM. Cloud pricing starts at $0.53 per hour, averaging $1.66 per hour across six providers, making it economical for development.

Low 70W TDP and PCIe form factor fit edge servers or laptops, ideal for real-time applications like video analytics with modest FP16 needs at 8.1 TFLOPS.