Maximizing Value with NVIDIA A40 & RTX A6000

For many people and organizations, the cost of high-end hardware is prohibitive when it comes to tasks like fine-tuning large language models (LLMs) and other AI workloads. It may not make sense to purchase a super-powered machine like the NVIDIA A100 or H100 when a more affordable option exists and can get the job done just about the same.

For instance, the NVIDIA A40 and RTX A6000 GPUs are incredibly attractive options for the more budget-conscious user – at least when compared to such expensive higher-end machines! Not only do they balance performance and cost, but they're also far more readily available than the A100 and H100 and can scale AI projects quickly.

NVIDIA A40 & RTX A6000: Similarities and Differences

The A40 and A6000 are both professional-grade GPUs, well suited for high-performance computing. The A40 is intended for server environments and data centers while the A6000 is designed for desktop workstations, but otherwise they're quite similar with just a few minor differences.

Both GPUs are Ampere architecture-based with a PCIe Gen 4.0 interface and have 48GB of GDDR6 RAM including error-correction code (ECC). However, the A40 offers 696 GB/s of peak memory bandwidth while the A6000 provides a touch more at 768 GB/s of bandwidth – as well as a slightly higher clock speed.

The two GPUs are tailor-made to handle demanding, large-scale AI workloads, with each featuring 10,752 CUDA cores (shading units), 84 second-gen RT cores, and 336 third-gen Tensor cores. Both include hardware support for a fine-grained structured sparsity feature, which can be used to accelerate inference and other deep learning workloads.

The A40 is passively cooled and employs bidirectional airflow, allowing air to move in either direction through the heatsink. This makes it better suited for use in servers. The A6000, on the other hand, has active cooling. Both GPUs use up quite a bit of power with a maximum power consumption of 300 watts.

The A40 has three display outputs, although they are disabled by default since the GPU is configured out of the box to support virtual graphics and compute workloads in virtualized environments. This makes it highly suitable for cloud-based applications and services, as it can easily deliver high-performance graphics and compute capabilities to remote users. The A6000 has four display ports that are enabled by default but are not active when using virtual GPU software.

Unlike the NVIDIA A100 and H100, the A40 and A6000 do not support Multi-Instance GPU (MIG), so it's not possible to run separate and fault-isolated workloads in parallel on the same physical GPU. However, their memory can be expanded by integrating a second GPU using NVLink technology, which allows the two to pool resources and operate as one unit with reduced latency and a combined memory of 96GB – plenty for most uses!

Benefits and Uses

Because the A40 and A6000 GPUs are so well suited to cloud environments and are more readily available than higher-end hardware, they allow organizations to scale their AI initiatives in a cost-effective and operationally efficient manner. And now, with the introduction of 10x GPU servers, the A40 and A6000 can be deployed in extremely powerful configurations to undertake AI projects that are even more ambitious and require significant computational resources.

According to some benchmarks, the A6000 can run about 10% faster than the A40 overall, due to its higher clock speed and memory bandwidth. But this advantage must be evaluated against the features of the A40, such as its secure and measured boot with hardware root of trust; NEBS Level 3 compliance (making it ideal for use in a wide array of network and telecom applications where stability and reliability are critical); and superior suitability to server environments.

Based on specs and performance, some examples of appropriate uses for the A40 and A6000 GPUs include:

• AI and Deep Learning Workflows – Training sophisticated neural networks, fine-tuning LLMs, running AI inference at scale, and deploying AI applications across various sectors like healthcare and finance.
• Scientific Research and Engineering Simulations – Running detailed simulations, modeling, data analysis, and computer-aided engineering (CAE) tasks in areas like climate study, bioinformatics, and the automotive, aerospace, and manufacturing industries.
• Advanced Visualization – Performing tasks where rapid rendering and visual fidelity are paramount, such as professional content creation and graphic design, virtual production, broadcast-grade streaming, real-time visual effects, and animation for film and game studios.

The Bottom Line

Ultimately, the NVIDIA A40 and RTX A6000 are an excellent choice for organizations and professionals who may not want to pay a supremely high price for the NVIDIA A100 or H100 – or who are happy to make the trade-off between faster processing times and lower cost plus availability – while still enjoying the ability to tackle the largest workloads in AI, visual computing, and data science.

All of that being said, the A40 and A6000 do still cost a pretty penny! Fortunately, purchasing your own hardware isn't a requirement to get started with these powerful machines. Here at Vast.ai, with low-cost cloud GPU rental through our platform, you can access a wide range of compute power across our network of hosts around the world, anytime, anywhere.

We offer the best prices for GPU rental, with on-demand pricing as low as $0.12/hr for the A40 and $0.50/hr for the RTX A6000 – and interruptible instances available via spot auction-based pricing for even more savings. We're proud of our mission to help democratize AI and ensure that its benefits are available to all!

[The table below compares various features and specs between the NVIDIA A40 and RTX A6000, with differences highlighted.]