Nvidia L4 vs A100, L40S, A10 and H100: which to choose

Picking a GPU for AI workloads used to mean choosing the biggest one you could afford. That does not work anymore. The Nvidia L4 vs A100 question comes up constantly, and it usually widens into a bigger one: L4, A100, L40S, A10, and H100 all show up in the same conversation. However, each one is built for a different job.

Choose wrong, and you either overpay for compute you don’t need or buy a GPU that cannot run your model at all. This blog breaks down the real differences, the workload each GPU fits best, and the operational costs that do not show up on a spec sheet.

Nvidia L4 vs A100, L40S and A10 differences

Before picking a GPU, it helps to see all five side by side. The two things that matter most are memory bandwidth and NVLink support. Everything else follows from those two.

The H100 and A100 use HBM (high bandwidth memory), stacked in layers to move data fast. LLM inference and training are memory-bound, meaning the GPU spends most of its time waiting for data, not processing it. That is why HBM makes such a large practical difference for big models.

The L40S, L4, and A10 use GDDR6, the same memory family used in consumer graphics cards. It is slower per byte, but far cheaper to manufacture. That cost difference is exactly why these three GPUs exist. They trade some raw bandwidth for a much lower price per GPU.

Which GPU for which workload?

The right GPU depends on three questions: 

• what you are running, 
• how big the model is, and 
• whether you need to scale across multiple GPUs.

Below are the answers to these questions:

Training a model from scratch, or fine-tuning a large model: Use the A100 or H100. Training needs high memory bandwidth and NVLink, since the job usually spans multiple GPUs working as one pool. The H100 is the faster option where budget allows. The A100 remains a solid choice for teams already running Ampere-based infrastructure.

Inference on models in the 7B to 14B parameter range: Use the L4. With FP8 quantization, the L4 comfortably serves models this size at a fraction of the cost per request of an A100. This is the sweet spot Nvidia designed it for.

Inference on larger models, 30B to 70B parameters, at scale: Use the L40S. Its 48GB of VRAM means you can often run two L40S GPUs for less than the cost of a single 80GB A100, giving you 96GB of pooled memory for large open-source models.

Video, audio, and media pipelines: Use the L4. It has dedicated NVDEC and NVENC hardware for video decode and encode, something the A100 and H100 do not have. Whisper transcription, video classification, and AV1 encoding all run well on L4 because the video hardware and the Tensor Cores work in parallel instead of competing for the same resources.

Light enterprise graphics or virtual workstations on a tight budget: Use the A10 if you already own it, but treat it as a GPU on its way out. It draws over 150W and lacks FP8 support. The L4 does the same job on 72W with newer Ada Lovelace features, making it the practical replacement for new deployments.

Multi-instance GPU (MIG) partitioning for multi-tenant workloads: Use the A100. MIG lets you split one physical A100 into isolated virtual GPUs, useful for serving multiple clients or teams off shared hardware with guaranteed performance. The L4, L40S, and A10 do not support MIG.

Operational realities in the spec sheet

Spec sheets tell you what a GPU can do in isolation. They do not tell you what it costs to actually run one in production. These four factors decide the real cost more often than the benchmark numbers do.

1. The bandwidth wall

The L40S has more raw TFLOPS of compute than the older A100. On paper, that looks like a clear win. In practice, LLM inference at large batch sizes or long context lengths is bandwidth-bound, not compute-bound. The A100’s HBM2e memory, at roughly 2,039 GB/s versus the L40S’s 864 GB/s, lets it keep feeding data to the compute cores fast enough to actually use that compute. This is why teams running high-throughput inference sometimes find the older A100 outperforming the newer L40S once batch sizes grow.

2. The NVLink gap and the PCIe bottleneck

The H100 and A100 connect to each other through NVLink, a direct high-speed bridge that lets multiple GPUs share memory and work as one large unit. The L40S, L4, and A10 have no NVLink connector. If you want to pool four or eight of them, they have to talk over the server’s standard PCIe lanes.

This creates a hidden cost. Running L40S or L4 GPUs at scale often means buying server chassis with specialized PCIe switches to stop that communication from becoming a bottleneck. A cheap motherboard will not do it. Before pricing a multi-GPU L40S or L4 deployment, price the chassis too.

3. VRAM per dollar

For teams running open-source models on a budget, the real question is not which GPU is fastest. It is how much usable memory you get per rupee spent. Two 48GB L40S GPUs often cost less combined than one 80GB A100, and together they give you 96GB of VRAM, enough to comfortably fit a 70B parameter model. This is the calculation behind why the L40S is treated as a budget option for large open-source models, even though it lacks NVLink.

4. Power and density

The L4 draws just 72W. That is small enough to matter at scale. A team running many inference nodes can pack far more L4 GPUs into the same rack and cooling budget than they could with A100s or H100s, which draw 250W to 700W depending on the variant. If your constraint is rack space or cooling capacity rather than raw model size, the L4’s power draw becomes a deployment advantage in its own right, not just a cost saving.

FP8 and quantization

FP8 is an 8-bit floating point format. In plain terms, it lets a model run using half the memory of the standard 16-bit format, with a similar level of accuracy for most inference tasks.

The L4, L40S, and H100 support FP8 natively, since they are built on the newer Ada Lovelace and Hopper architectures. The A100 and A10 do not, because they predate this feature.

For an IT head or CFO evaluating GPU spend, this matters beyond the technical detail. FP8 support means you can serve the same model on cheaper, lower-power hardware than an equivalent FP16 setup would require. It is one of the clearest ways the L4 GPU price punches above its price point for inference workloads.

Quick decision guide

Here’s a quick workload-based guide to choosing the right GPU:

Conclusion

There is no single best GPU among the L4, A100, L40S, A10, and H100. Each one is built for a different point on the training-to-inference spectrum, and the right choice depends on model size, budget, and how the workload scales.

The part that gets missed most often is operational cost. A GPU that looks cheaper on the spec sheet can end up costing more once NVLink, chassis requirements, and power draw are factored in. The GPU that fits your actual workload, not the one with the biggest number on paper, is the one that keeps your infrastructure bill predictable.