L4 vs MI300X

MI300X delivers dramatically higher compute throughput: FP16 at 1307 TFLOPS compared to L4's 121 TFLOPS, accelerating deep learning training by over 10 times for models like large language models. FP32 performance of 163 TFLOPS on MI300X surpasses L4's 30.3 TFLOPS, benefiting general-purpose floating-point tasks in simulations.

Memory bandwidth represents the largest gap: 5300 GB/s on MI300X versus 300 GB/s on L4, enabling much larger batch sizes and reducing data transfer bottlenecks in training pipelines. This allows MI300X to process datasets that exceed L4's 24 GB VRAM capacity.

For inference, FP8 performance reaches 2614 TFLOPS on MI300X against 242 TFLOPS on L4, supporting high-throughput serving of quantized models. However, L4's 72W TDP contrasts with MI300X's 750W, making L4 preferable for power-constrained environments where density matters over peak speed.

The L4 GPU fits inference-heavy workloads in cost-sensitive cloud setups. Its pricing starts at $0.32 per hour with an average of $0.78 per hour across 11 providers, combined with a 72W TDP and PCIe form factor, enables dense deployments without excessive cooling or power costs.

Edge computing or smaller-scale fine-tuning benefits from L4's 24 GB VRAM and 300 GB/s bandwidth, where availability trumps raw performance.

MI300X stands out for large-scale LLM training requiring 192 GB HBM3 VRAM and 5300 GB/s bandwidth. Its FP16 performance of 1307 TFLOPS handles massive models that overwhelm L4's 24 GB capacity.

High-performance computing tasks leverage MI300X's FP32 at 163 TFLOPS and Infinity Fabric interconnect for multi-GPU scaling.