NVIDIA H100 vs L40S: AI‑Focused GPU Comparison and Practical Alternatives

At the time of writing, the NVIDIA H100 80 GB PCIe is priced around $32,000 on retailers such as CDW and has been out of stock for roughly six months, underscoring its premium status and strong demand among AI users.

The article introduces the NVIDIA L40S, a variant of the graphics‑oriented L40, as an alternative for enterprises running mixed workloads, and proceeds to examine three primary high‑end inference GPUs: the NVIDIA A100, H100, and the new L40S, while skipping the lower‑end L4 24 GB.

The A100 and H100 models are flagship GPUs of their respective generations. Because the discussion focuses on PCIe cards rather than SXM modules, the most noticeable differences are NVLink support and power consumption; SXM cards are designed for roughly twice the power draw and interconnect via NVLink and NVSwitch.

The A100 PCIe launched in 2020 with a 40 GB variant and was later updated to an 80 GB version in mid‑2021, remaining popular years later.

The H100 PCIe is a low‑power version aimed at mainstream servers. Within the H100 family there are variations; the PCIe version reduces performance, power draw, and some interconnect speeds (e.g., NVLink) compared with the SXM counterpart.

The L40S differs significantly: it is based on the data‑center visual GPU L40 built on NVIDIA’s Ada Lovelace architecture, but its tuning is shifted toward AI workloads rather than pure graphics.

While retaining the L40’s ray‑tracing cores, DisplayPort output, and AV1‑capable NVENC/NVDEC, the L40S allocates more power to AI‑related clock domains and supports NVIDIA Transformer Engine and FP8 precision, which dramatically reduces memory footprint and bandwidth requirements.

In a comparative chart (included in the original article), the L40S is shown to have lower memory capacity and bandwidth than the A100/H100, but its FP8 support can offset these disadvantages for many AI tasks.

Pricing-wise, the H100 is roughly 2.6 × more expensive than the L40S at the time of writing, and the L40S is considerably more available, making it a practical choice for organizations that cannot wait for H100 stock.

Additional considerations include vGPU support (L40S supports vGPU 16.1, while H100 is limited to vGPU 15), the lack of MIG on L40S, and the L40S’s roughly half‑power consumption compared with SXM5‑based systems, which benefits rack‑level power budgeting.

Overall, although the L40S does not match the raw FP64 performance of the H100, its FP8 and Transformer Engine capabilities, lower cost, and easier deployment make it a viable alternative for many AI training and inference scenarios.

The article concludes that while the H100 remains the top‑of‑the‑line AI accelerator, the L40S offers a compelling balance of performance, availability, and price for enterprises seeking to scale AI workloads without the premium price and scarcity of the H100.