NVIDIA A40 Vs RTX A6000: A Detailed Comparison

NVIDIA A40 Vs RTX A6000: A Detailed Comparison

For many individuals and organizations, the expense of high-end hardware for tasks like fine-tuning large language models (LLMs) and other AI workloads can be prohibitive. Investing in super-powered machines like the NVIDIA A100 or H100 may not be necessary when more affordable options can perform nearly as well.

For example, the NVIDIA A40 and RTX A6000 GPUs are highly attractive options for budget-conscious users. They offer a balance between performance and cost, are more readily available than the A100 and H100, and can scale AI projects quickly.

NVIDIA A40 & RTX A6000: Comparing Features

The A40 and A6000 are both professional-grade GPUs designed for high-performance computing. While the A40 is intended for server environments and data centers, and the A6000 is for desktop workstations, they share many similarities with only minor differences.

Both GPUs are based on the Ampere architecture with a PCIe Gen 4.0 interface and have 48GB of GDDR6 RAM with an error correction code (ECC). The A40 has a peak memory bandwidth of 696 GB/s, whereas the A6000 offers slightly more at 768 GB/s and has a marginally higher clock speed.

Both are equipped to handle demanding AI workloads, featuring 10,752 CUDA cores, 84 second-gen RT cores, and 336 third-gen Tensor cores. They also support fine-grained structured sparsity to accelerate inference and other deep-learning tasks.

The A40 uses passive cooling with bidirectional airflow, which is ideal for servers, while the A6000 uses active cooling. Both GPUs consume a maximum of 300 watts of power.

The A40 includes three display outputs, which are disabled by default to support virtual graphics and compute workloads in virtualized environments, making it suitable for cloud applications. The A6000 has four display ports enabled by default, which are inactive when using virtual GPU software.

Unlike the A100 and H100, the A40 and A6000 do not support Multi-Instance GPU (MIG), preventing parallel fault-isolated workloads on the same GPU. However, they can be paired using NVLink technology to combine resources with reduced latency and a total memory of 96GB.

Benefits and Applications

The A40 and A6000 GPUs are well-suited for cloud environments and more accessible than high-end hardware, allowing organizations to scale AI initiatives cost-effectively and efficiently. They can be deployed in powerful configurations, such as 10x GPU servers, for ambitious AI projects requiring substantial computational resources.

Benchmarks suggest the A6000 runs about 10% faster than the A40 due to its higher clock speed and memory bandwidth. However, the A40 offers features like a secure and measured boot with hardware root of trust and NEBS Level 3 compliance, making it ideal for network and telecom applications requiring stability and reliability.

Appropriate Uses for A40 and A6000 GPUs

AI and Deep Learning Workflows: Training neural networks, fine-tuning LLMs, running AI inference at scale, and deploying AI applications in various sectors, including healthcare and finance.

Scientific Research and Engineering Simulations: Running simulations, modeling, data analysis, and computer-aided engineering (CAE) tasks in fields like climate study, bioinformatics, and the automotive, aerospace, and manufacturing industries.

Advanced Visualization: Tasks requiring rapid rendering and visual fidelity, such as professional content creation, graphic design, virtual production, streaming, visual effects, and animation for film and game studios.

The Bottom Line

The NVIDIA A40 and RTX A6000 are excellent choices for those who prefer not to invest in the high cost of the NVIDIA A100 or H100. They balance performance and affordability while handling large AI, visual computing, and data science workloads.

Although the A40 and A6000 are still costly, owning them isn't necessary to access their power. Decentralized Platforms like Spheron can offer low-cost cloud GPU rentals, providing access to a wide range of compute power globally. Spheron aims to democratize AI, making its benefits accessible to everyone.

Comparison Table: NVIDIA A40 vs. RTX A6000

FeatureA40RTX A6000
Memory Size48 GB48 GB
Memory TypeGDDR6GDDR6
Error-Correcting Code (ECC)YesYes
CUDA Cores10,75210,752
3rd-gen Tensor Cores336336
2nd-gen RT Cores8484
Render Output Units (ROPs)112112
FP16 (Half) Performance37.42 TFLOPS38.71 TFLOPS
FP32 (Float) Performance37.42 TFLOPS38.71 TFLOPS
FP64 (Double) Performance584.6 GFLOPS604.8 GFLOPS
Pixel Rate194.9 GPixel/s201.6 GPixel/s
Texture Rate584.6 GTexel/s604.8 GTexel/s
Memory Interface384-bit384-bit
Memory Bandwidth696 GB/s768 GB/s
Clock Speed1305 MHz1410 MHz
Boost Speed1740 MHz1800 MHz
Memory Clock1812 MHz (14.5 Gbps effective)2000 MHz (16 Gbps effective)
Slot WidthDual-slotDual-slot
Thermal SolutionPassiveActive
Power Consumption (Total Board Power)300 W300 W
Suggested Power Supply700 W700 W
System InterfacePCIe 4.0 x16PCIe 4.0 x16
Display Ports3x DisplayPort 1.44x DisplayPort 1.4
vGPU SupportYes (default)Yes
NEBS ReadyYes (Level 3)No
Secure and Measured Boot with Hardware Root of TrustYes (optional)No
Graphics APIsDirectX 12.07, Shader Model 5.17, OpenGL 4.68, Vulkan 1.18DirectX 12.07, Shader Model 5.17, OpenGL 4.68, Vulkan 1.18
Compute APIsCUDA, DirectCompute, OpenCL, OpenACCCUDA, DirectCompute, OpenCL
Release DateOct. 5th, 2020Oct. 5th, 2020

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