L40S vs MI325X

Memory specifications dominate real-world impacts: the MI325X's 256 GB HBM3e VRAM supports models far larger than the L40S's 48 GB GDDR6X limit, enabling training or inference on massive LLMs without multi-GPU sharding. Bandwidth at 6000 GB/s for the MI325X versus 864 GB/s for the L40S allows larger batch sizes, reducing per-iteration time in training by minimizing data transfer bottlenecks.

Floating-point performance reveals architecture priorities. The L40S shows FP16 at 362 TFLOPS exceeding FP32 at 91 TFLOPS, suiting inference-heavy tasks where half-precision accelerates throughput. The MI325X balances both at 1307 TFLOPS, favoring training workflows that demand FP32 precision for gradient computations without sacrificing speed.

FP8 performance underscores inference edges: 724 TFLOPS on L40S versus 2614 TFLOPS on MI325X, with higher TDP at 750W enabling sustained peaks on MI325X for quantized deployments. Overall, MI325X excels in memory-bound scenarios, L40S in power-efficient, available inference.

The L40S suits immediate cloud deployments for inference and fine-tuning. With pricing from $0.40 per hour across 18 offers and 350W TDP, it integrates via PCIe 4.0 into existing clusters without high power demands. Its 362 TFLOPS FP16 and 724 TFLOPS FP8 handle quantized LLM serving efficiently within 48 GB VRAM limits.

Choose L40S for cost-sensitive projects or PCIe-based systems where availability trumps raw specs.

The MI325X targets memory-intensive training of giant models. Its 256 GB HBM3e VRAM and 6000 GB/s bandwidth support enormous batch sizes and un-sharded LLMs, with 1307 TFLOPS FP32 enabling precise gradient updates.

Opt for MI325X in high-performance computing clusters using OAM and Infinity Fabric, once available, for workloads exceeding 48 GB model footprints.