A100 SXM4 80GB vs L40

The A100 SXM4 80GB dominates in FP16 performance at 312 TFLOPS: this tensor core capability accelerates mixed-precision training for large language models far beyond the L40's 90.5 TFLOPS. Conversely, the L40 achieves parity between FP16 and FP32 at 90.5 TFLOPS each, suiting inference workloads and graphics rendering where single-precision compute prevails.

Memory specifications reveal a clear divide: A100's 2039 GB/s bandwidth and 80 GB HBM2e capacity enable larger batch sizes in training, minimizing data loading bottlenecks and supporting models exceeding 48 GB. The L40's 864 GB/s and 48 GB GDDR6 constrain it for memory-bound tasks, though its Ada Lovelace tensor cores offer per-watt gains.

Power efficiency tilts toward L40 at 300W TDP versus A100's 400W: this results in lower operational costs for sustained inference, while A100's NVLink interconnect boosts multi-GPU training throughput.

Select the A100 SXM4 80GB for memory-intensive LLM training where 80 GB HBM2e VRAM handles models like GPT-3 variants without swapping: its 2039 GB/s bandwidth sustains large batches, and 312 TFLOPS FP16 speeds convergence. Multi-GPU setups benefit from NVLink, ideal for datacenter-scale scientific simulations requiring over 48 GB capacity.

Opt for the L40 in cost-sensitive inference deployments: its balanced 90.5 TFLOPS FP16 and FP32 performance, at $0.67 per hour starting price, delivers efficient real-time serving. The 300W TDP reduces cooling needs, and Ada Lovelace architecture enhances graphics tasks like Stable Diffusion with 48 GB GDDR6 suiting most batch sizes.