Choosing GPUs for LLM Inference: A100 vs. H100 vs. L40S

Large language model inference is increasingly GPU-bound, with compute, memory bandwidth, and power efficiency driving hardware choices. NVIDIA’s A100, H100, and L40S GPUs represent distinct generations and design points addressing LLM serving in enterprise and cloud environments. This guide examines the architectural differences, benchmark performance, and cost considerations for each GPU to support evidence-based infrastructure decisions.

Architectural overview and specs

The NVIDIA A100, based on the Ampere architecture (released 2020), features up to 80GB HBM2e memory, 6912 CUDA cores, and supports Tensor Float 32 (TF32) and bfloat16 formats. It has a PCIe Gen4 interface and TDP up to 400W.

The H100 represents the Hopper architecture (released 2022) and includes significant advancements: 80GB HBM3 memory, 16GB/s higher memory bandwidth than A100, 14592 CUDA cores, and new transformer engine hardware blocks accelerating FP8 precision. It supports PCIe Gen5 and NVLink 4 and has a TDP of 350W to 700W depending on the variant.

The L40S is based on the Ada Lovelace architecture (released 2023) and targets inference and visualization workloads. It includes 48GB GDDR6 memory, 9728 CUDA cores, and optimized Tensor Cores for FP16 and INT8 inference. The card trades some raw compute for lower power consumption (~300W) and cost-efficiency.

Performance comparison on LLM inference workloads

LLM inference performance depends heavily on mixed precision compute capabilities, memory bandwidth, and support for quantization formats. NVIDIA’s Ampere A100 handles FP16 and INT8 well but lacks specialized FP8 support present in Hopper H100, which can accelerate transformer inference up to 2x faster, as measured by NVIDIA MLPerf benchmarks.

The H100’s transformer engine introduces FP8 precision inference, enabling higher throughput at minimal accuracy loss. For example, in MLPerf inference v3.0, H100 achieved approximately 2.1x the throughput of A100 on the GPT-3 175B model at comparable latency targets.

The L40S provides competitive inference throughput on models up to 30 billion parameters when using INT8 and FP16 quantization, but it underperforms A100 and H100 on very large models requiring higher memory capacity or bandwidth. Its advantage lies in cost-effective inference for midsize LLMs with lower power demands.

Cost and power efficiency considerations

At the time of writing, A100 80GB GPUs list starting at approximately $11,000 per unit on NVIDIA’s official pricing, with server builds typically around $150,000 to $200,000 including PCIe Gen4 motherboard and CPU. The H100 80GB PCIe variant retails near $30,000 each, reflecting its generational improvement and added features.

The L40S is positioned as a lower-cost alternative, priced roughly at $3500 to $4500 per unit OEM, which benefits large-scale inference deployments constrained by capital expense. Its power envelope of 300W reduces operational expenditures relative to A100 or H100, whose TDPs can exceed 400W.

Infrastructure teams must balance acquisition cost, power usage effectiveness (PUE), and throughput. TCO modeling from IDC suggests that while H100 accelerates inference, the cost per generated token can be 1.7x higher than A100 due to the higher purchase price unless workloads fully exploit FP8 capabilities.

Deployment scenarios and workload match

For enterprises deploying large-scale LLM inference workloads—such as GPT-3 class or larger—where latency and throughput are critical, the H100 is generally the optimal choice due to its transformer engine and memory bandwidth. It is suited for cloud providers, hyperscalers, and research labs demanding cutting-edge performance.

Organizations running smaller LLMs or commercial generative AI models with tighter budgets and less aggressive latency constraints may find the L40S delivers a better price-performance balance. Its lower capacity restricts model size to mid-tier LLMs, but it reduces power and cost.

The A100 remains relevant in mixed workloads combining training and inference or for deployments with existing Ampere infrastructure. Its price-performance ratio is mature and stable, with wide software ecosystem support including CUDA, Triton, and TensorRT.