If you are comparing the NVIDIA L4 and the A100, here is the short answer: pick the L4 for efficient inference and video work, and pick the A100 for training, fine-tuning and larger models. The two cards come from different generations and were built for different jobs. This guide covers the differences that matter in practice.
L4 vs A100: specifications side by side
| Specification | NVIDIA L4 | NVIDIA A100 40GB (PCIe) |
|---|---|---|
| Architecture | Ada Lovelace (2023) | Ampere (2020) |
| Memory | 24 GB GDDR6 ECC | 40 GB HBM2 ECC |
| Memory bandwidth | 300 GB/s | 1,555 GB/s |
| CUDA cores | 7,424 | 6,912 |
| Power (TDP) | 72 W | 250 W |
| FP8 support | Yes | No |
| Multi-Instance GPU (MIG) | No | Up to 7 instances |
| Hardware video encode | AV1, HEVC, H.264 | None |
Memory and model sizes
VRAM decides which models you can load at all. A useful rule of thumb for language models:
- FP16: about 2 GB per billion parameters
- 8-bit: about 1 GB per billion parameters
- 4-bit: about 0.6 GB per billion parameters
On top of that, the context window (the KV cache) needs memory that grows with prompt length and the number of parallel requests.
With 24 GB, the L4 runs a 7B or 8B model in FP16 with room for context, and models up to about 30B in 4-bit. The A100 40GB runs a 13B model in FP16 or a 30B-class model in 8-bit, with more headroom for long contexts. A 70B model in 4-bit needs around 42 GB before context, so it does not fit on one A100 40GB, but you can split it across two cards.
Throughput and memory bandwidth
The L4 has more CUDA cores and a newer architecture, yet the A100 usually serves LLMs faster. The reason is memory bandwidth. Generating each token means reading the model weights from memory, so data movement often limits speed more than compute does.
The A100 moves about five times more data per second than the L4. It generates tokens faster on the same model and holds up far better with large batches and many concurrent users. The L4 suits moderate traffic, internal tools and APIs. Its native FP8 support also helps: engines that use FP8 cut memory use and narrow part of the gap on supported models.
Power and efficiency
The L4 draws 72 W; the A100 draws 250 W. For inference that fits in 24 GB, the L4 delivers much more work per watt, and its single-slot, low-profile design fits a standard 1U server. That makes it a good fit for always-on services with steady load. The A100 uses more power but finishes heavy jobs sooner, so for training, total time and energy per run matter more than power draw alone.
Video workloads
This is where the cards differ most. The L4 encodes and decodes AV1, HEVC and H.264 in hardware, so it handles transcoding, streaming and video analytics on one low-power card. The A100 has no hardware encoder, which makes it a poor choice for video. If you are replacing older T4 cards, the L4 is their natural successor in the same low-power role. It also works well for computer vision and speech recognition.
Training and fine-tuning
Training needs memory for weights, gradients, optimizer states and activations, often several times more than inference. The A100 was built for this: more memory, higher bandwidth, bigger batches. MIG lets you split one card into up to seven isolated instances for experiments or several small teams. The L4 can handle light LoRA or QLoRA fine-tuning on small models, but if fine-tuning is part of your regular work, choose the A100.
Cost considerations
The L4 costs less to rent and to run, so for inference that fits in 24 GB it usually gives the lowest cost per request. The A100 costs more, but it can replace several smaller cards when you need larger models, higher throughput or training capacity. An A100 that mostly idles while serving a small model wastes money; a training job squeezed onto an L4 wastes time.
How to choose
- Serving 7–8B models, or up to ~30B in 4-bit? Choose the L4.
- Transcoding, streaming or video analytics? Choose the L4.
- Always-on service with a tight power budget? Choose the L4.
- Training or regular fine-tuning? Choose the A100.
- Larger models, long contexts or many concurrent users? Choose the A100.
L4 and A100 servers at IPHOST
IPHOST runs dedicated GPU servers in its own ISO/IEC 27001 certified data center in Chișinău, Moldova. Our NVIDIA L4 dedicated server is built on an HPE DL360 Gen10 with an Intel Xeon Gold 6248 (20 cores), 128 GB RAM and 2x 1.6 TB SAS-SSD.
For training and larger models, the 2x NVIDIA A100 dedicated server uses an HPE DL380 Gen10 with two Xeon Gold 6230R processors (52 cores in total) and 256 GB RAM. The two cards give you 80 GB of GPU memory to split a model across. If you want more memory on a single card, look at the NVIDIA A40 server with 48 GB.
Every GPU server includes a 1 Gbps port and 30 TB of traffic. We deliver on pre-order within 14 days and bill quarterly, with up to 14% off for 12 months. If you would rather not manage the stack yourself, see our LLM hosting service.
Conclusion
The L4 and the A100 do not really compete. The L4 is an efficient, low-power card for inference, vision and video; the A100 is a high-bandwidth card for training, fine-tuning and larger models. Start from your model size and workload, and the choice usually becomes clear.
