For most inference workloads, the L40S is the more cost effective choice while the A100 remains preferable for higher throughput or training heavy needs. The L40S, built on the Ada Lovelace architecture, offers 48 GB of GDDR6 memory at about 864 GB/s of bandwidth, strong FP8 support, and lower power draw around 350 watts, making it well suited for serving models up to roughly 30B to 40B parameters and for mixed workloads that combine inference with computer vision or graphics tasks. The A100 offers up to 80 GB of HBM2e at about 2 TB/s of bandwidth, which gives it a real advantage for larger models, bigger KV caches, or training and fine tuning jobs that benefit from higher memory bandwidth and NVLink connectivity across multiple GPUs. If the primary use case is serving a mid sized LLM with moderate concurrency, or running many smaller models cost efficiently, the L40S usually wins on cost per token. If the workload involves training, fine tuning, or serving very large models at high concurrency, the A100's bandwidth and memory capacity make it the safer pick. Nanobase AI compares both options against actual model size and expected concurrency before recommending a GPU for a client's inference stack.
Side-by-side specification comparison
| Spec | L40S | A100 (80 GB) |
|---|---|---|
| Architecture | Ada Lovelace | Ampere |
| Memory | 48 GB GDDR6 | 80 GB HBM2e |
| Bandwidth | ~864 GB/s | ~2 TB/s |
| FP8 Transformer Engine | Yes | No |
| Power draw | ~350 W | Higher, HBM-based |
| NVLink | Limited/no NVLink | Yes, 3rd gen |
| Best suited for | Mid-size LLM serving, mixed vision/graphics workloads | Larger models, training, high-bandwidth workloads |
The core tradeoff is bandwidth and capacity versus efficiency and cost: A100 doubles L40S's memory and more than doubles its bandwidth, while L40S counters with native FP8 support and a meaningfully lower power footprint.
Where L40S wins on cost per token
For serving models up to roughly 30B to 40B parameters at moderate concurrency, the L40S's FP8 support directly reduces memory footprint and increases effective throughput per GPU compared to A100's FP16/TF32-only path, often producing a better cost per token despite A100's raw bandwidth advantage. L40S also handles mixed workloads well, since it was originally built as a graphics and compute hybrid card, making it a sensible fit for organizations combining LLM inference with computer vision or rendering tasks on the same fleet.
Where A100 wins despite the lower efficiency
A100's larger 80 GB memory pool and roughly 2 TB/s of bandwidth matter once model size or KV cache requirements exceed what fits comfortably in 48 GB, and its NVLink support gives it a real advantage for training or fine-tuning jobs that benefit from fast multi-GPU communication, which L40S is not built for. Any workload doing serious gradient computation across multiple GPUs, or serving models that need more than 48 GB even after quantization, points toward A100 over L40S regardless of the efficiency gap.
Decision table by scenario
| Scenario | Better choice |
|---|---|
| Serving a 13B–34B model to moderate concurrent users | L40S |
| Serving a 70B model that needs more than one GPU either way | A100 (or current-gen alternative) |
| Mixed inference + computer vision / rendering workloads | L40S |
| Fine-tuning or full training with multi-GPU parallelism | A100 |
| Budget-constrained inference-only deployment | L40S |
| Existing A100 fleet with spare capacity | A100 |
Frequently asked questions
Does L40S support multi-GPU training well?
Not particularly. L40S lacks the NVLink connectivity that makes A100 (and H100) suited to multi-GPU training, so it is best used for inference or single-GPU fine-tuning rather than large-scale distributed training.
Is A100 worth choosing over L40S purely for inference in 2026?
Generally not, unless the model size specifically requires A100's larger memory pool. For most inference-only workloads under roughly 40B parameters, L40S delivers better cost per token thanks to native FP8 support and lower power draw.
Can L40S and A100 run in the same server?
Physically and logically they can coexist in a data center, but mixing them within a single multi-GPU job is impractical given the interconnect and precision differences; most teams run them as separate pools for different workload types.
Which draws less power, L40S or A100?
L40S draws considerably less power at roughly 350 W compared to A100's higher HBM-based power envelope, which simplifies cooling and power planning for L40S deployments.
How Nanobase AI helps
Nanobase AI, an enterprise AI engineering company with engineering headquarters in Silicon Valley, compares L40S and A100 against actual model size and expected concurrency before recommending a GPU for a client's inference stack, so the choice reflects real workload economics rather than a generic hardware hierarchy. Learn more about our GPU sizing services.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.