A100 80GB PCIe vs. Tesla V100-PCIE-32GB: Professional AI GPUs Compared

The GPU you choose can make a monumental difference in artificial intelligence (AI) and machine learning (ML). The right graphics processing unit (GPU) can accelerate your AI workloads, enabling faster training times, more accurate models, and higher productivity. Two of NVIDIA’s most prominent offerings in this space are the A100 80GB PCIe and the Tesla V100-PCIE-32GB. Both are designed for professional AI applications but cater to different needs and use cases.

This article compares these two powerhouse GPUs, exploring their architecture, performance, energy efficiency, scalability, and more to help you determine which is best suited for your AI projects.

NVIDIA Ampere Architecture (A100 80GB PCIe)

The A100 80GB PCIe is built on NVIDIA’s Ampere architecture, representing a significant leap in AI processing power. This architecture introduces several new features, including the third-generation Tensor Cores, which deliver up to 20 times the AI performance of the previous generation. The A100 also supports Multi-Instance GPU (MIG) technology, allowing a single A100 to be partitioned into up to seven independent instances, each with its resources.

Architecture: Ampere
GPU Memory: 80 GB HBM2e
Memory Bandwidth: 2,039 GB/s
CUDA Cores: 6,912
Tensor Cores: 432 (Third-generation Tensor Cores)
FP16 Performance: 312 TFLOPS
FP32 Performance: 19.5 TFLOPS
FP64 Performance: 9.7 TFLOPS
Tensor Performance: 1,248 TFLOPS
Multi-Instance GPU (MIG): Supported, up to 7 instances
PCIe Generation: PCIe 4.0
Power Consumption: 250-300 watts
Form Factor: Full-height, full-length (FHFL) dual-slot
Cooling: Passive
Interconnect: NVLink (Optional, 600 GB/s with NVLink)
NVLink Support: Yes, up to 12 links (for multi-GPU configurations)
Software Support: CUDA 11.x and later, cuDNN, TensorRT, DeepStream, AI Frameworks (TensorFlow, PyTorch, etc.)
Recommended PSU: 650W or greater
Operating Temperature: 0°C to 50°C
Dimensions: 10.5" (267 mm) x 4.4" (112 mm) x 1.4" (35 mm)
Weight: ~1.5 kg (3.3 lbs)

Advantages of A100 80GB PCIe

Unmatched performance in AI and deep learning.
Large memory capacity and bandwidth.
Advanced scalability features (MIG).
Future-proof architecture.

Disadvantages of A100 80GB PCIe

High cost.
Higher power consumption.

NVIDIA Volta Architecture (Tesla V100-PCIE-32GB)

The Tesla V100-PCIE-32GB, on the other hand, is based on the Volta architecture. Volta was revolutionary, introducing the first generation of Tensor Cores designed explicitly for AI workloads. The V100 remains a highly capable GPU, particularly in data centers where reliability and performance are paramount. Despite being older than the A100, the Volta architecture still holds its ground in various applications, thanks to its robust design and high efficiency.

Architecture: Volta
GPU Memory: 32 GB HBM2
Memory Bandwidth: 900 GB/s
CUDA Cores: 5,120
Tensor Cores: 640 (First-generation Tensor Cores)
FP16 Performance: 125 TFLOPS
FP32 Performance: 15.7 TFLOPS
FP64 Performance: 7.8 TFLOPS
Tensor Performance: 125 TFLOPS
PCIe Generation: PCIe 3.0
Power Consumption: 250 watts
Form Factor: Full-height, full-length (FHFL) dual-slot
Cooling: Passive
Interconnect: NVLink (Optional, 300 GB/s with NVLink)
NVLink Support: Yes, up to 6 links (for multi-GPU configurations)
Software Support: CUDA 10.x and later, cuDNN, TensorRT, DeepStream, AI Frameworks (TensorFlow, PyTorch, etc.)
Recommended PSU: 600W or greater
Operating Temperature: 0°C to 50°C
Dimensions: 10.5" (267 mm) x 4.4" (112 mm) x 1.4" (35 mm)
Weight: ~1.47 kg (3.2 lbs)

Advantages of Tesla V100-PCIE-32GB

Lower cost.
Proven reliability in data-center environments.
More energy-efficient.

Disadvantages of Tesla V100-PCIE-32GB

Lower performance compared to A100.
Less future-proof as AI workloads evolve

A100 80GB PCIe vs. Tesla V100-PCIE-32GB

Here's a comparison chart of A100 80GB PCIe vs. Tesla V100-PCIE-32GB:

Feature	Tesla V100 PCIe 32 GB	A100 PCIe 80 GB
Architecture	Volta (2017−2020)	Ampere (2020−2022)
GPU Code Name	GV100	GA100
Market Segment	Workstation	Desktop
Release Date	27 March 2018 (6 years ago)	28 June 2021 (3 years ago)
Pipelines / CUDA Cores	5120	6912
Core Clock Speed	1230 MHz	No data
Boost Clock Speed	1380 MHz	1410 MHz
Number of Transistors	21,100 million	54,200 million
Manufacturing Process	12 nm	7 nm
Power Consumption (TDP)	250 Watt	250 Watt
Texture Fill Rate	441.6 GTexels/s	609.1 GTexels/s
Floating-Point Performance	14,131 GFLOPS	No data
Interface	PCIe 3.0 x16	PCIe 4.0 x16
Length	No data	267 mm
Width	2-slot	2-slot
Supplementary Power Connectors	2x 8-pin	8-pin EPS
Memory Type	HBM2	HBM2e
Maximum RAM Amount	32 GB	80 GB
Memory Bus Width	4096 Bit	5120 Bit
Memory Clock Speed	1752 MHz	3.2 GB/s
Memory Bandwidth	897.0 GB/s	2,039 GB/s
DirectX	12 (12_1)	N/A
Shader Model	6.4	N/A
OpenGL	4.6	N/A
OpenCL	1.2	3.0
Vulkan	1.2.131	N/A
CUDA	7.0	8.0
Chip Lithography	12 nm	7 nm

Key Architectural Differences and Impacts on Performance

The most notable difference between the A100 and V100 lies in the Tensor Cores. The A100’s third-generation Tensor Cores offer significantly better performance for mixed-precision workloads, common in AI and deep learning tasks. The architecture of the A100 is also more flexible, thanks to MIG technology, which can optimize resource allocation for diverse workloads. In contrast, the V100’s Tensor Cores are less advanced but still highly effective for many applications, particularly those that do not require the latest in AI performance enhancements.

Memory Capacity and Bandwidth

A100 80GB PCIe: Expansive Memory Capacity

One of the standout features of the A100 80GB PCIe is its massive memory capacity. With 80GB of HBM2e memory, it can handle larger datasets and more complex models than its predecessors. The A100 also benefits from a memory bandwidth of 2,039 GB/s, allowing for rapid data transfer between the GPU and memory, which is crucial for maintaining high performance in data-intensive tasks.

Tesla V100-PCIE-32GB: High Bandwidth for Data-Intensive Tasks

The Tesla V100-PCIE-32GB offers 32GB of HBM2 memory, which, while smaller than the A100, still provides substantial capacity for most AI workloads. Its memory bandwidth is also impressive, at 900 GB/s. While the V100’s memory is less than half that of the A100, it remains sufficient for many AI models, particularly in environments where datasets are not excessively large.

Comparison of Memory Capacity and Bandwidth Impact on AI Workloads

When comparing the two, the A100 has the advantage regarding memory capacity and bandwidth. This makes it better suited for larger models and datasets, which are becoming increasingly common in AI research and industry. However, the V100’s memory can still handle a wide range of applications, especially when budget constraints are considered.

Performance Benchmarks

Floating-Point Performance (FP16, FP32, FP64)

Regarding floating-point performance, the A100 80GB PCIe outshines the Tesla V100-PCIE-32GB. The A100 delivers 312 TFLOPS in FP16, 19.5 TFLOPS in FP32, and 9.7 TFLOPS in FP64. By contrast, the V100 offers 125 TFLOPS in FP16, 15.7 TFLOPS in FP32, and 7.8 TFLOPS in FP64. These numbers indicate the A100’s superior ability to handle complex mathematical operations, which is critical for AI and ML tasks.

Tensor Core Performance

The A100’s Tensor Cores can achieve up to 1,248 TFLOPS, making it vastly more powerful in AI-specific workloads than the V100, which delivers around 125 TFLOPS. This disparity highlights the A100’s efficiency in processing AI tasks, particularly those involving deep learning and neural network training.

AI and Deep Learning Performance Metrics

In real-world AI and deep learning benchmarks, the A100 consistently outperforms the V100. For instance, in tasks such as training large-scale neural networks or performing inference on massive datasets, the A100’s superior architecture and larger memory capacity result in significantly faster processing times and higher throughput.

While both GPUs are highly capable, the A100 shows a clear lead in nearly all performance benchmarks, making it the better choice for organizations looking to maximize their AI processing capabilities. However, the V100 remains competitive in environments where cutting-edge performance is not the primary requirement.

Energy Efficiency

Power Consumption of A100 80GB PCIe

Under typical AI workloads, the A100 80GB PCIe consumes around 250-300 watts of power. Despite its higher performance, it maintains a relatively efficient energy profile thanks to advancements in the Ampere architecture.

Power Consumption of Tesla V100-PCIE-32GB

The Tesla V100-PCIE-32GB, in comparison, consumes about 250 watts, making it slightly more efficient in terms of power consumption relative to its performance. This can be an important consideration in large-scale data centers where energy costs are significant.

Efficiency in High-Performance Computing (HPC) Environments

In HPC environments, the energy efficiency of a GPU directly impacts the total cost of ownership (TCO). The A100, while slightly more power-hungry, offers much higher performance, which can offset its higher power consumption by reducing the time required to complete tasks.

Use Cases

A100 80GB PCIe: Best for Large-Scale AI and ML Models

The A100 80GB PCIe excels in large-scale AI and ML models, particularly those requiring extensive parallel processing and large datasets. Its massive memory and superior Tensor Core performance make it ideal for cutting-edge research, autonomous systems, and large-scale enterprise AI applications.

Tesla V100-PCIE-32GB: Ideal for Data-Center Applications and Research

The Tesla V100-PCIE-32GB is still a formidable option for many data center applications and research environments. It is particularly well-suited for tasks that don’t require the latest AI performance but still need reliable, high-powered computing resources.

Industry-Specific Use Cases

For industries like healthcare, automotive, and finance, where AI applications are becoming increasingly sophisticated, the A100’s advanced capabilities make it the preferred choice. The V100, however, remains valuable in academia and smaller research labs where the latest technology might not be necessary.

Scalability

A100 80GB PCIe: Scalability in Multi-GPU Configurations

The A100’s scalability is one of its key strengths. With support for multi-GPU configurations, it can be deployed in massive clusters to tackle the most demanding AI workloads. Its ability to partition resources using MIG also adds to its versatility, allowing organizations to optimize their hardware utilization more effectively.

Tesla V100-PCIE-32GB: Performance in Distributed Systems

The V100 also scales well in distributed systems, making it suitable for large-scale data-center deployments. However, it lacks the advanced scalability features of the A100, particularly in terms of resource partitioning and flexibility.

Impact on Large-Scale AI Projects

For large-scale AI projects, the A100 is the clear winner in terms of scalability. Its ability to handle more extensive datasets and more complex models makes it better suited for projects that require significant computational resources.

Software Ecosystem

Both the A100 and V100 are fully compatible with NVIDIA’s CUDA platform and cuDNN libraries, which are essential for developing AI applications. CUDA and cuDNN provide the necessary tools to optimize GPU performance for a wide range of AI and deep learning frameworks. Both GPUs support popular AI frameworks such as TensorFlow, PyTorch, and MXNet. However, the A100 has been optimized for these frameworks, offering better performance and faster processing times for AI and deep learning tasks.

NVIDIA has provided numerous optimizations and tools for both the A100 and V100, including support for TensorRT and DeepStream SDK. These tools help developers get the most out of their GPUs, but the A100, being the newer model, benefits from the latest advancements and optimizations.

Longevity and Future-Proofing

The A100 is designed with the future in mind. Its cutting-edge technology ensures that it will remain relevant for years to come, making it a sound investment for organizations looking to stay ahead in AI. While the V100 is older, it still has a place in AI research, particularly in settings where the latest hardware isn’t necessary. However, as AI workloads continue to grow in complexity, the V100 may struggle to keep up with the demands of the future.

From an investment perspective, the A100 offers more longevity and a higher return on investment (ROI) due to its advanced features and superior performance. The V100, while still valuable, may see diminishing returns as AI technology continues to evolve.

Pricing and Availability

The A100 80GB PCIe is priced at a premium, reflecting its advanced capabilities and superior performance. As of 2024, it remains one of the most expensive GPUs on the market, which can be a barrier for smaller organizations. The Tesla V100-PCIE-32GB is more affordable, making it a more accessible option for organizations with tighter budgets. However, its lower price comes with a trade-off in terms of performance and future-proofing.

Both GPUs are widely available, though the A100 is more commonly found in enterprise environments and large-scale data centers. The V100, while still available, may be harder to find as NVIDIA phases out older models in favor of newer technology.

Customer Support and Warranty

NVIDIA provides comprehensive support for the A100, including access to the latest drivers, software updates, and professional services. The A100 also comes with a robust warranty, ensuring that organizations can rely on it for critical applications.

Support for the V100 is still available, though it may not be as extensive as for the A100. As the V100 is an older model, organizations may find that support and warranty options are more limited compared to the newer A100. The A100 generally comes with a longer and more comprehensive warranty, reflecting its status as a premium product. The V100, while still covered, may have shorter warranty periods and fewer service options.

Environmental Considerations

The A100’s energy footprint is higher due to its increased power consumption, but it compensates for this with its superior performance, which can reduce the overall time and energy required to complete tasks. The V100, being slightly more energy-efficient, has a lower energy footprint, making it a better option for organizations looking to minimize their environmental impact while still achieving high performance.

NVIDIA has made strides in improving the energy efficiency of its GPUs, and both the A100 and V100 benefit from these initiatives. Organizations focused on sustainability may find that either GPU aligns with their corporate environmental goals, though the A100 offers better performance-per-watt.

Industry Feedback and Reviews

The A100 has received widespread acclaim from industry professionals for its unparalleled performance, flexibility, and scalability. It is often described as the gold standard for AI and deep learning applications. The Tesla V100 is still highly regarded, particularly in academic and research settings. While it is no longer the top performer, it is praised for its reliability and cost-effectiveness.

Conclusion

When comparing the A100 80GB PCIe and the Tesla V100-PCIE-32GB, it is clear that both GPUs have their strengths. The A100 offers superior performance, scalability, and future-proofing, making it the best choice for cutting-edge AI applications. The Tesla V100, while older, still holds its ground as a reliable and cost-effective option for many AI and data-center tasks.

Ultimately, choosing these two GPUs depends on your specific needs, budget, and long-term goals. If your focus is on maximizing AI performance and staying ahead of the curve, the A100 is the clear winner. However, if you need a more affordable, reliable solution that still delivers solid performance, the Tesla V100 remains a strong contender.

FAQs

What are the key differences between A100 80GB PCIe and Tesla V100-PCIE-32GB?
- The A100 offers higher performance, larger memory capacity, and better scalability, while the V100 is more cost-effective and energy-efficient.
Which GPU is better for deep learning tasks?
- The A100 80GB PCIe is better suited for deep learning tasks due to its advanced Tensor Cores and higher memory capacity.
How do these GPUs compare in terms of energy efficiency?
- The Tesla V100 is slightly more energy-efficient, but the A100’s superior performance may offset its higher power consumption in terms of overall efficiency.
Is the Tesla V100 still a good investment in 2024?
- Yes, the Tesla V100 is still a good investment for many AI workloads, particularly in environments where the latest technology is not required.
How does the price of these GPUs reflect their performance?
- The A100 is more expensive but offers significantly higher performance, making it worth the investment for organizations with demanding AI workloads. The V100 is more affordable but with a trade-off in terms of performance and future-proofing.