GPU Comparison

NVIDIA A100 vs L4

Compare NVIDIA A100 and L4 across architecture, GPU memory, bandwidth, AI inference, training, infrastructure requirements and practical workload fit.

Daya ShankarLast verified: August 11, 2026Research methodology

NVIDIA Ampere

A100

GPU Memory

80 GB HBM2e

Bandwidth

2.039 TB/s

Ampere data-center accelerator for AI training, inference, data analytics and HPC.

Explore NVIDIA A100

NVIDIA Ada Lovelace

L4

GPU Memory

24 GB GDDR6

Bandwidth

300 GB/s

Low-power Ada accelerator optimized for inference, video, graphics and mainstream deployment.

Explore NVIDIA L4

A100 vs L4 at a Glance

Start with workload fit, then validate the choice against the exact cloud configuration and pricing available to you.

Choose A100 when

You need 80 GB memory.

Choose L4 when

24 GB is sufficient.

Compare the full workload

Memory, bandwidth, precision, interconnects, media features, power and cloud price can all change the right answer.

NVIDIA A100 vs NVIDIA L4 Specifications

H100, H200 and A100 use SXM figures. B200 uses current NVIDIA HGX B200 specifications. L40S and L4 use their native PCIe card specifications, so each product is represented in its primary deployment form.

Specification	NVIDIA A100	NVIDIA L4
Architecture	NVIDIA Ampere	NVIDIA Ada Lovelace
GPU Memory	80 GB HBM2e	24 GB GDDR6
Memory Bandwidth	2.039 TB/s	300 GB/s
FP32	19.5 TFLOPS	30.3 TFLOPS
TF32 Tensor Core	312 TFLOPS*	120 TFLOPS*
FP16 / BF16 Tensor Core	624 TFLOPS*	242 TFLOPS*
FP8 Tensor Core	Not natively supported	485 TFLOPS*
FP4 Tensor Core	Not natively supported	Not natively supported
NVLink	600 GB/s	Not supported
MIG	Up to 7 MIGs @ 10 GB	Not supported
Maximum Power	400 W standard SXM	72 W
Form Factor	SXM	Low-profile single-slot PCIe

* Tensor Core values marked with an asterisk are NVIDIA sparse specifications where applicable; dense performance is lower.

What Is the Main Difference Between NVIDIA A100 and NVIDIA L4?

A100 is an Ampere high-performance compute accelerator; L4 is an Ada low-power inference and media GPU. Their specifications reflect those different roles.

A100 SXM provides 80 GB HBM2e at 2.039 TB/s plus NVLink and MIG. L4 provides 24 GB GDDR6 at 300 GB/s in a 72 W low-profile PCIe card.

L4 has newer fourth-generation Tensor Cores and FP8 support, which can make it efficient for compatible inference. A100 remains far better suited to large training, HPC and workloads needing more memory.

Memory Capacity and Bandwidth

These specifications affect model fit, KV-cache headroom, batch size and memory-bound workloads.

GPU Memory

80 GB HBM2e vs 24 GB GDDR6

A100L4

Memory Bandwidth

2.039 TB/s vs 300 GB/s

A100L4

A100 vs L4 for LLM Inference

L4 is a strong efficiency choice for small and medium models that fit in 24 GB, and it supports FP8. A100's 80 GB HBM2e gives much more room for model weights and KV cache.

A100 vs L4 for Training and HPC

A100 is designed for training and HPC, with high-bandwidth memory, NVLink, MIG and strong FP64/TF32 capabilities. L4 can support lighter development and inference but is not intended as a large-scale A100 replacement.

A100 vs L4 for Video and Power Efficiency

L4's 72 W TDP, low-profile form factor and dedicated video engines are important advantages for inference and media density. A100 consumes far more power and requires data-center-class platform support.

Which GPU Fits Your Workload?

Use this as directional guidance. Benchmark your own model and software stack before making a large infrastructure commitment.

Workload	A100	L4	Direction
Large-model training	Best fit	Light workloads	A100
Large LLM inference	Best fit	Limited by 24 GB	A100
Small / quantized inference	Strong	Best efficiency fit	L4
FP8 inference	Not native	Supported	L4
HPC	Best fit	Not primary focus	A100
Video AI	General compute	Best fit	L4
Power-constrained deployment	High power	Best fit	L4

Cloud Pricing

Compare Current Provider Pricing

There is no single cloud price for either GPU. Rates vary by provider, region, server configuration, billing model and commitment. Compare current provider offers after you know which hardware class fits the workload.

A100 providers

Compare A100 pricing

View A100

L4 providers

Compare L4 pricing

View L4

Final Decision

Should You Choose NVIDIA A100 or NVIDIA L4?

Choose NVIDIA A100 if:

You need 80 GB memory.
Training or HPC is the main workload.
You require NVLink or MIG.
The workload needs much higher memory bandwidth.

Choose NVIDIA L4 if:

24 GB is sufficient.
You want low-power inference.
FP8 inference and video acceleration are useful.
You need a low-profile single-slot PCIe card.

A100 vs L4 FAQs

Common questions about choosing between these NVIDIA GPUs.

Which is better, NVIDIA A100 or L4?

Choose A100 for training, HPC, 80 GB workloads, NVLink and MIG. Choose L4 for efficient inference, AI video and compact 72 W PCIe deployment when 24 GB is sufficient.

What is the main difference between A100 and L4?

A100 uses Ampere with 80 GB HBM2e and 2.039 TB/s memory bandwidth, while L4 uses Ada Lovelace with 24 GB GDDR6 and 300 GB/s. Tensor Core generation, precision support, interconnects and power can also differ.

Is A100 or L4 better for LLM inference?

L4 is a strong efficiency choice for small and medium models that fit in 24 GB, and it supports FP8. A100's 80 GB HBM2e gives much more room for model weights and KV cache.

Which GPU is better for AI training, A100 or L4?

How much memory do A100 and L4 have?

NVIDIA A100 provides 80 GB HBM2e, while NVIDIA L4 provides 24 GB GDDR6. Memory capacity alone does not determine performance, so bandwidth, precision support and workload behavior should also be considered.

Which is cheaper to rent, A100 or L4?

Cloud rental pricing for A100 and L4 varies by provider, region, configuration and billing model. Check current provider pricing rather than assuming one GPU is always cheaper.

Sources & Verification

Hardware specifications were checked against official NVIDIA product pages and documentation.

Last verified: August 11, 2026 · View our data source standards

GPU Profile

NVIDIA A100

See specifications, cloud providers, pricing and workload fit for A100.

View A100

GPU Profile

NVIDIA L4

See specifications, cloud providers, pricing and workload fit for L4.

View L4