NVIDIA L40 vs NVIDIA RTX A6000: Which One Should You Choose for AI Workloads?

When choosing a GPU for your AI workloads, you’re likely to feel confused. With plenty of cloud GPUs for AI available on the market offering the compute power and features needed to handle demanding AI tasks, it’s easy to get overwhelmed. The NVIDIA L40 and the NVIDIA RTX A6000 are considered affordable options due to their relatively lower pricing. Both provide massive compute capabilities and 48 GB of memory, but they’re built on different architectures. So, which one is better suited for your AI needs? Let’s find out.

AI Training Performance: NVIDIA L40 vs NVIDIA RTX A6000

AI training (with large datasets) demands high compute throughput, memory and precision flexibility. The NVIDIA L40, with 18,176 CUDA cores and 181.05 TFLOPS FP16 performance, outpaces the NVIDIA RTX A6000’s ~155 TFLOPS. With structured sparsity, NVIDIA L40 reaches over 362 TFLOPS, compared to NVIDIA RTX A6000’s ~310 TFLOPS, showing its efficiency for modern AI training workflows.

Similar Read: How NVIDIA L40 Accelerates AI Training 

AI Inference Performance: NVIDIA L40 vs NVIDIA RTX A6000

AI inference requires low latency and high throughput, especially for generative AI and large language models (LLMs). The NVIDIA L40 is designed for data centre workloads, including inference, with 48 GB GDDR6 memory and support for FP8 precision. The NVIDIA RTX A6000 also offers 48 GB GDDR6 but lacks FP8 support, limiting its efficiency in mixed-precision inference. The NVIDIA L40’s 864 GB/s memory bandwidth, compared to NVIDIA RTX A6000’s 768 GB/s also enhances real-time processing, reducing bottlenecks in high-resolution inference.

However, both GPUs get a performance boost on Hyperstack with:

• High-Speed Networking (up to 350 Gbps): Ideal for multi-GPU distributed training

• Ephemeral NVMe storage: Removes data bottlenecks during training/inference

Please note that High-Speed Networking for NVIDIA L40 and the NVIDIA RTX A6000 is for contracted customers only.

NVIDIA L40 vs NVIDIA RTX A6000: Which One to Choose

Depending on your AI workload, you can opt for either of the GPUs. 

Choose the NVIDIA L40 If:

• You need top-tier performance for training and inference.

• Your workflows can leverage FP8 precision for faster, more efficient processing.

• You’re future-proofing your stack for modern AI advancements.

Deploy NVIDIA L40 for $1.00/hr in Minutes on Hyperstack.

Choose the NVIDIA RTX A6000 If:

• Your workloads are lighter or less demanding.

• Budget constraints outweigh the need for maximum performance.

• You’re prototyping or experimenting with smaller models.

Deploy NVIDIA RTX A6000 for $0.50/hr in Minutes on Hyperstack.

Conclusion

In conclusion, for heavy AI training or high-throughput inference, the NVIDIA L40 is the ideal GPU as it can handle demanding AI workloads. For moderate workloads, prototyping or budget-limited cases, the NVIDIA RTX A6000 is still an excellent GPU that can get the job done and might be the more economical choice if absolute performance is not required. Our real cloud environment further ensures that whichever you choose, you can benefit from our high-speed networking and storage to remove bottlenecks and get the best performance out of the GPUs.

Explore Related Resources

• Comparing NVIDIA H100 PCIe vs SXM: Performance, Use Cases and More

• NVIDIA A100 PCIe vs NVIDIA A100 SXM: A Comprehensive Comparison

• NVIDIA RTX A6000 vs NVIDIA A100 Across Various Workloads

FAQs

Which GPU is better for AI training, NVIDIA L40 or NVIDIA RTX A6000?

The NVIDIA L40 offers higher FP16 performance and FP8 support, making it better for AI training.

Does the NVIDIA RTX A6000 support FP8 precision?

No, the NVIDIA RTX A6000 lacks FP8 support, which can impact mixed-precision inference efficiency.

Which GPU is more cost-effective for AI workloads?

The NVIDIA RTX A6000 is more budget-friendly at $0.50/hr, while the NVIDIA L40 offers superior performance at $1.00/hr.

How quickly can I access these GPUs on Hyperstack?

You can deploy either GPU in minutes on Hyperstack here for on-demand AI workloads.