Choosing between the NVIDIA L40 and A100 comes down to more than raw specs. Each GPU targets different AI workloads; picking the wrong one can mean running out of resources or paying for unused performance.
This guide breaks down the key architectural differences, benchmark results, and practical use cases to help you make an informed decision.
Quick Comparison Table
| Specification | NVIDIA L40 | NVIDIA A100 (80 GB) |
|---|---|---|
| Architecture | Ada Lovelace | Ampere |
| VRAM | 48 GB GDDR6 | 80 GB HBM2e |
| CUDA cores | 18,176 | 6,912 |
| Tensor cores | 568 | 432 |
| Memory Bandwidth | 864 GB/s | 2,039 GB/s |
| FP16 Tensor Performance | 362 TFLOPS | 312 TFLOPS |
| TDP | 300 W | 400 W |
| Interconnect | PCIe Gen 4 | PCIe Gen 4 / NVLink (SXM4) |
| NVLink Support | No | Yes (SXM variant) |
| Form Factor | PCIe | PCIe or SXM |
| Primary Use Case | - Small-scale training - Inference |
- Large-scale training - HPC |
NVIDIA L40 vs A100 Specs
Understanding the technical foundation of each GPU makes it easier to predict real-world behavior before running a single workload.
Architecture: Ampere vs. Ada Lovelace
The A100 is built on NVIDIA's Ampere architecture, released in 2020, and remains a relevant data center GPU for large-scale training.
The L40 is based on the newer Ada Lovelace architecture from 2022, which brings significant improvements to tensor core efficiency and introduces 4th gen Tensor Cores with FP8 support.
Ada Lovelace also adds hardware-accelerated ray tracing and DLSS capabilities. These features are irrelevant for pure AI workloads but reflect the generational advancement of the underlying silicon.
Memory Bandwidth
This is where the A100 holds its most decisive lead. The A100 80 GB uses HBM2e memory with 2,039 GB/s of bandwidth, more than double the L40's 864 GB/s from GDDR6.
High memory bandwidth is the primary bottleneck in transformer workloads where large weight matrices must be streamed repeatedly during forward and backward passes.
For memory-bound operations, the A100's bandwidth advantage translates directly into faster iteration times.
Latency and Concurrency
The L40 is better positioned for low-latency, high-concurrency inference. Its Ada Lovelace architecture improves the efficiency of smaller, parallelized operations that are common in serving scenarios.
When running multiple concurrent inference requests at moderate batch sizes, the L40's newer architecture can sustain throughput more efficiently than the A100, even with its lower raw bandwidth.
PCIe vs SXM
The L40 is available only in a PCIe form factor, making it easy to deploy in standard server configurations. The A100 comes in PCIe for simple configurations and SXM4 with NVLink connectivity for higher bandwidth.
For multi-GPU training setups, the SXM4 A100 makes multi-GPU viable, while single-node PCIe deployments put the two GPUs on more equal footing.
Power Consumption
The L40 has a 300W TDP versus the A100's 400W, a 25% reduction that compounds meaningfully in large-scale deployments.
Lower power draw also reduces cooling requirements and can translate directly into lower per-hour cloud costs. For teams optimizing infrastructure spend, the L40's efficiency profile is worth considering.
Explore current pricing for each GPU:
<ul><li><a href="https://www.thundercompute.com/blog/nvidia-a100-pricing" target="_blank" rel="noopener">NVIDIA A100 pricing guide</a></li><li><a href="https://www.thundercompute.com/blog/nvidia-l40-pricing" target="_blank" rel="noopener">NVIDIA L40 pricing guide</a>.</li></ul>
Performance
Spec comparisons set the stage, but benchmark results from real AI workloads reveal how each GPU performs under pressure.
Large Language Model (LLM) Training
The A100 is the stronger choice for LLM training, primarily because of its memory bandwidth and NVLink support.
Training large models requires frequent, high-volume data movement between memory and compute units, and the A100's HBM2e architecture is designed accordingly.
Independent benchmarks running GPT-style model training at the 7B and 13B parameter scale consistently show the A100 SXM4 delivering 30-50% higher throughput than the L40 in distributed configurations using NVLink.
For single-GPU training of smaller models, the gap narrows considerably, with the L40's newer Tensor Core generation compensating for lower memory bandwidth. The A100's advantage in distributed configurations stems from its third-generation NVLink architecture.
Inference Throughput
The picture shifts for inference, especially at the batch sizes common in production APIs.
The L40's Ada Lovelace architecture handles FP8 inference more efficiently, and its lower memory footprint per request allows more concurrent batches to fit within its 48 GB VRAM. At smaller batch sizes, A100 inference throughput is highly sensitive to concurrency, leaving meaningful capacity unused in single-stream or small-batch serving scenarios.
Benchmarks from MLPerf and independent evaluations of models like LLaMA-2 7B show the L40 achieving competitive or superior tokens-per-second at batch sizes of 1 to 16 compared to the PCIe A100.
At very large batch sizes, the A100's memory bandwidth advantage re-emerges, but most real-world serving scenarios fall within the range where the L40 performs favorably.
Scaling with NVLink and NVSwitch
Multi-GPU scaling is where the A100 SXM4 becomes difficult to match. NVLink 3.0 on the SXM4 A100 delivers up to 600 GB/s of GPU-to-GPU bandwidth per GPU in an eight-way configuration, enabling near-linear scaling for tensor parallelism across large models.
The L40 has no NVLink support and must rely on PCIe Gen 4 for inter-GPU communication, which limits scaling efficiency for models that exceed single-GPU VRAM capacity. For teams training models at the 30B parameter scale and above across multiple GPUs, the A100 SXM4 with NVSwitch fabric is in a different category than the L40.
Use Cases
Both GPUs are well-suited to AI workloads. The decision narrows based on the specific demands of your pipeline.
When to Choose the L40
The L40 is the right choice to prioritize inference throughput and cost efficiency.
Teams running production inference APIs, fine-tuning smaller models, or experimenting with a range of model sizes will find the L40 cost-effective. It combines Ada Lovelace efficiency, FP8 support, and lower power draw to deliver strong performance per dollar.
It is also a natural fit for workloads that run entirely within a single GPU, where NVLink scaling is not a factor. Organizations deploying a large number of GPU nodes will benefit from the L40's lower thermal and power footprint at scale.
When to Choose the A100
The A100 is the better option for large-scale distributed training, especially when using the SXM4 configuration with NVLink.
Research teams training foundational models, running complex scientific simulations, or working with workloads that require more than 48 GB of VRAM per GPU will find the A100's 80 GB HBM2e and multi-GPU bandwidth indispensable.
It also remains a strong general-purpose option for teams that alternate between training and inference, since its raw memory capacity accommodates a wider range of model sizes without architectural optimization.
Final Thoughts on NVIDIA L40 vs A100
Both the L40 and the A100 are capable GPUs for serious AI workloads.
The A100 leads in large-scale distributed training scenarios where memory bandwidth and NVLink scaling matter most. The L40 is the stronger choice for inference-heavy workloads and cost-sensitive deployments.
Thunder Compute offers access to both GPUs so you can match hardware to workload without committing to fixed infrastructure. Try Thunder Compute GPUs today and run your workloads on the right hardware from the start.