Artificial Intelligence (AI) has made rapid strides in recent years, dramatically changing industries from health to finance and entertainment. However, these new-gen technologies require parallel processing of large datasets, which is not possible with a traditional IT setup. Hence, businesses have been looking to transition from the traditional IT setup to a GPU-led infrastructure.
NVIDIA dominates the GPU market with an impressive 88% share, leading innovation in graphics and computing. Its diverse GPU lineup caters to a wide range of use cases, with standout products like the NVIDIA A100 and L4 GPUs, designed for exceptional performance in specific tasks.
Let’s discuss both in detail so you can choose the best GPU for your workload.
Understanding the NVIDIA A100 GPU
The NVIDIA A100 GPU was introduced in May 2020. Its massive computing capacity makes the A100 GPU ideal for data centers and enterprise-scale AI development. Built on the Ampere architecture, the A100 has been optimized for versatility and efficiency.
Therefore, it is efficient at handling training workloads and AI inference. Moreover, the A100 has become indispensable for high-performance computing (HPC) applications due to its advanced features and capabilities.
A100 Key Features
Here are some major features of the NVIDIA A100.
- High Memory Bandwidth: The NVIDIA A100 has up to 80GB of HBM2e memory and supports large datasets and complex models. It is perfect for deep learning training.
- Third-Generation Tensor Cores: These Tensor Cores are the best in the industry for mixed-precision (FP16, FP32, and TF32) calculations that are imperative for AI workloads.
- Multi-Instance GPU (MIG) Technology: The A100 comes with MIG, which divides the GPUs into up to seven isolated instances, enabling simultaneous processing of different workloads on a single card.
- NVLink and NVSwitch technology: These interconnect technologies provide high-bandwidth connections between GPUs, ensuring scalability in multi-GPU setups.
- High Double-Precision Performance: The A100 can provide up to 19.5 teraflops of FP64 Tensor core performance when performing scientific computations that demand accuracy.
Top Use Cases of A100:
- AI Training: The A100 can support the training of state-of-the-art model workloads like GPT-4, ResNet, and BERT, which demand substantial computational resources.
- HPC Applications: The NVIDIA A100 is suitable for scientific simulations, molecular modeling, and climate prediction as it provides exceptional FP64 performance.
- Enterprise AI Workloads: The A100’s versatility and scalability facilitate large-scale data analysis, recommendation, and autonomous systems.
Understanding the NVIDIA L4 GPU
NVIDIA’s L4 GPU, introduced in 2023, marks a big move from heavyweight training workloads to low-power inference and multimedia processing. For businesses that need cost-effective ways to deploy AI at scale, the L4 is the perfect solution. It is built on the Ada Lovelace architecture, making it a specialist for low-latency AI inferencing and video-centric applications.
Key Features of the NVIDIA L4
- Compact Design: Single-slot PCIe form factor enables quick integration with existing equipment.
- Energy Efficiency: With a maximum power draw of only 72W, the L4 was tailored for deployment where power and cooling resources are limited.
- Fourth-Generation Tensor Cores: Optimized for mixed-precision (FP16 and INT8) inference, this hardware delivers high throughput in real-time scenarios.
- AV1 Hardware Encoding/Decoding: The L4’s advanced multimedia capabilities make it perfect for video streaming, transcoding, and content filtering.
- GDDR6 Memory: The NVIDIA L4 has 24GB of memory. Although insufficient for complex models, it is enough for inference and multimedia processing tasks.
Primarily Used for
- AI Inference: The L4 is ideal for real-time recommendations, chatbots, and personalized systems.
- Media Processing: The NVIDIA L4 supports AV1 encoding/decoding for video streaming, editing, and broadcasting.
- Edge AI Deployment: The NVIDIA L4’s small size and low power make it well-suited for edge computing in industries like retail and healthcare.
Comparison of NVIDIA A100 and NVIDIA L4
Let’s look at the specifications of NVIDIA A100 and L4.
| Feature | A100 | L4 |
| FP64 | 9.7 TFLOPS | – |
| FP64 Tensor Core | 19.5 TFLOPS | – |
| FP32 | 19.5 TFLOPS | 30.3 teraFLOPs |
| Tensor Float 32 (TF32) | 156 TFLOPS | 120 teraFLOPS |
| BFLOAT16 Tensor Core | 312 TFLOPS | 242 teraFLOPS |
| FP8 Tensor Core | – | 485 teraFLOPs |
| FP16 Tensor Core | 312 TFLOPS | 242 teraFLOPS |
| INT8 Tensor Core | 624 TOPS | 485 TOPs |
| GPU Memory | 80GB | 24GB |
| Memory Bandwidth | 2,039 GB/s | 300GB/s |
| Max Thermal Design Power (TDP) | 400W | 72W |
| Multi-Instance GPU | Up to 7 MIGs @ 10GB | – |
| Form Factor | PCIe/SXM | PCIe |
| Interconnect | NVIDIA® NVLink® Bridge for 2 GPUs: 600 GB/s ** PCIe Gen4: 64 GB/s | PCIe Gen4 x16 64GB/s |
Now, let’s discuss some vital aspects that will help you choose between the NVIDIA A100 and L4.
1. NVIDIA AI Training
NVIDIA A100: The A100 is a better platform than L4 for AI training. With its massive memory, inter-GPU connectivity, and Tensor Cores, the A100 can handle big datasets and complex models. Many large enterprises and research institutions rely upon it in training models like GPT and BERT.
NVIDIA L4: The L4 is simply not suited to training large models. Although the L4 could run lightly loaded training tasks, its architecture is built for inference performance and efficiency.
2. AI Inference
NVIDIA A100: The A100 can run inference efficiently. However, using it as a cab would be like using a Ferrari. The A100 is not a budget-friendly or energy-efficient option for only AI inference tasks.
NVIDIA L4: The L4 is specifically designed for inference, with very high performance yet low power consumption. Its fourth-generation Tensor Cores are optimized for INT8 and FP16 precision, commonly used in inference tasks.
3. Energy Efficiency
NVIDIA A100: The A100 consumes up to 400W, which makes it an energy-consuming GPU, suitable only for environments with capable cooling and power infrastructure.
NVIDIA L4: The L4’s TDP is just 72W, which means it’s substantially more energy efficient and will dramatically reduce operational costs in data centers and edge environments.
4. Graphics
NVIDIA A100: When it comes to Graphics, the A100 GPU falls short of NVIDIA L4. This is because the NVIDIA A100 does not offer integration with major Graphics APIs. However, it is well-suited for AI training. The A100 Supports CUDA and OpenCL, which is ideal for Machine Learning.
NVIDIA L4: The NVIDIA L4 is built for multimedia applications, like video rendering and image processing. It supports all major graphics GPU, like DirectX, OpenGL, and Vulkan. If you want a GPU for multimedia, the NVIDIA L4 wins by a long margin.
5. Cost-Effectiveness
NVIDIA A100: As the right solution for large AI models that require a huge amount of processing power, the A100 is a premium product. However, it involves extensive initial investment and operational costs.
NVIDIA L4: In terms of initial capital outlay and energy efficiency, L4 is a more affordable solution than A100. Therefore, businesses with budget restrictions that do not deal with complex AI models can choose NVIDIA L4.
A more cost-efficient method of adopting the GPUs is to opt for a cloud GPU service. Cloud GPU providers like AceCloud offer A100 and L4 GPUs on a pay-as-you-use pricing model. This way, you don’t need to purchase, deploy, and manage the GPU infrastructure on-premises, minimizing capital and operational costs.
Choosing the Right GPU for Your Workload
NVIDIA A100 and L4 GPUs offer advanced capabilities for different workloads. However, you must analyze your business type, scale, and industry before choosing a GPU.
Why Choose the NVIDIA A100
- Your workload involves training large-scale deep learning models.
- You need exceptional double-precision (FP64) performance for scientific simulations.
- Your infrastructure can leverage NVLink for multi-GPU scaling.
- Budget and power consumption are not primary concerns.
Why Choose the NVIDIA L4
- Your focus is on AI inference, particularly in real-time or low-latency applications.
- You prioritize energy efficiency and cost-effectiveness.
- Your workload includes video processing or multimedia applications.
- You require a compact GPU for edge deployments or space-constrained environments.
Conclusion
The A100 and L4 GPUs are two of the most popular GPUs offered by NVIDIA. However, choosing between the two depends on what you want them for. The NVIDIA A100 is ideal for AI training and Deep Learning. On the other hand, the NVIDIA L4 offers better multimedia and inference capabilities at a low cost. However, if you want to try out the GPU features before purchasing, you can opt for the free trial of a cloud GPU solution. This way, you can make a wise decision.
AceCloud is a cloud-based GPU provider that offers a wide range of GPUs, including A100, L4, L40s, and H100, at flexible pricing and 24/7 support. Book a free consultation with our experts now.
