L40S vs MI300X

Memory specifications define key advantages: the MI300X's 192 GB HBM3 supports larger batch sizes than the L40S's 48 GB GDDR6X, enabling training of models exceeding 70 billion parameters without multi-GPU sharding. Bandwidth amplifies this: 5300 GB/s on MI300X versus 864 GB/s on L40S reduces data transfer bottlenecks during inference, sustaining higher throughput for real-time applications.

FP16 performance favors the MI300X at 1307 TFLOPS over L40S's 362 TFLOPS, accelerating mixed-precision training by up to 3.6 times. FP32 at 163 TFLOPS versus 91 TFLOPS benefits scientific simulations requiring precise floating-point operations. FP8 reaches 2614 TFLOPS on MI300X against 724 TFLOPS on L40S, optimizing quantized inference for LLMs. Power draw reflects trade-offs: 750W TDP for MI300X demands robust cooling, while L40S's 350W suits denser deployments.

Interconnects highlight ecosystem differences: PCIe 4.0 on L40S versus Infinity Fabric and PCIe 5.0 on MI300X, potentially yielding lower latency in AMD clusters.

The L40S excels in cost-sensitive, available cloud inference tasks. With pricing from $1.65 per hour across three offers, it delivers 724 TFLOPS FP8 for efficient quantized LLM serving. Its 350W TDP and PCIe form factor enable easy integration into standard racks without high power overhead.

Smaller-scale fine-tuning or Stable Diffusion workloads benefit from 362 TFLOPS FP16 and 48 GB VRAM, avoiding overprovisioning.

The MI300X dominates large-scale LLM training due to 192 GB HBM3 and 1307 TFLOPS FP16. This configuration handles massive datasets with 5300 GB/s bandwidth, supporting batch sizes infeasible on 48 GB alternatives.

High-throughput scientific computing leverages 163 TFLOPS FP32 and Infinity Fabric interconnects for multi-node scaling.