Nvidia A100 80GB GPU

The Nvidia A100 graphics processor, an upgraded version of the Ampere chip, comes equipped with 80GB of graphics memory (VRAM), offering significantly enhanced performance compared to previous models. As the flagship of Nvidia's Ampere family

 

 this processor is designed to meet the demands of high-performance computing and advanced applications.

 

Article Content:

- Introduction to the A100 80GB Graphics Processor

- Comparison of Nvidia’s High-End Graphics Chips

- Applications of the A100 80GB Graphics Processor

- Compatible Brands with the A100 80GB Graphics Processor

- Servers and Storage Compatible with the Nvidia A100 80GB

 

Upgraded Ampere A100 Chip with 80GB of Memory

Nvidia has recently introduced a new flagship for its family of Ampere graphics chips. The latest model of the Ampere graphics chip is an enhanced version of the previous flagship, featuring increased graphics memory and a substantial improvement in bandwidth.

The new A100 model with 80GB of graphics memory retains many aspects of the 40GB model, including a boost clock speed of 1.41 GHz, a 5120-bit memory bus, single-precision compute power of 19.5 TFLOPS, and a thermal design power (TDP) of 400 watts. Both models are equipped with 6,192 CUDA cores and support NVLink 3.

The primary difference between the Ampere flagship chips is the maximum graphics memory, which has been increased from 40GB to 80GB in the new model. Additionally, the new model offers greater memory bandwidth. Official data shows that the 80GB model provides 3.2 gigabits per second (Gbps) of HBM2e bandwidth, compared to the 2.4 Gbps bandwidth of the 40GB model. The overall memory bandwidth across the HBM2 array is 2 terabytes per second (TB/s), whereas the 40GB A100 model offers 1.6 TB/s.

 

The 80GB A100 chip is constructed with six HBM2 packages, although Nvidia has disabled one of these packages to enhance the chip's overall performance. Each of the remaining five packages has a 1024-bit memory bus, resulting in a total memory bus width of 5120 bits. The 80GB model uses HBM2E packages instead of HBM2 to significantly enhance the chip's fundamental features. This model is designed for workloads requiring higher capacity and bandwidth.

The 80GB model, like the 40GB model, can support up to seven devices, providing each with 10GB of graphics memory. Nvidia plans to release this new graphics processor in the form of single Mezzanine Modular graphics cards, with HGX or DGX configurations.

Comparison of Nvidia High-End Graphics Chips

Features	A100 (80GB)	A100 (40GB)	V100
CUDA Cores (FP32)	6,912	6,912	5,120
Boost Clock Speed	1,410 MHz	1,410 MHz	1,530 MHz
Memory Clock Speed	3.2 Gbps HBM2e	2.4 Gbps HBM2	1.75 Gbps HBM2
Memory Bus Width	5,120-bit	5,120-bit	4,096-bit
Memory Bandwidth	2.0 TB/s	1.6 TB/s	900 GB/s
Graphics Memory	80 GB	40 GB	16 or 32 GB
Single-Precision Compute Power	19.5 TFLOPS	19.5 TFLOPS	15.7 TFLOPS
Double-Precision Compute Power	9.7 TFLOPS (half FP32 rate)	9.7 TFLOPS (half FP32 rate)	7.8 TFLOPS (half FP32 rate)
INT8 Tensor Performance	624 TOPS	624 TOPS	Unknown
FP16 Tensor Performance	312 TFLOPS	312 TFLOPS	125 TFLOPS
TF32 Tensor Performance	156 TFLOPS	156 TFLOPS	Unknown
Interconnect	NVLink 3, 12 links (600 GB/s)	NVLink 3, 12 links (600 GB/s)	NVLink 2, 6 links (300 GB/s)
GPU Processor	GA100, 825 mm²	GA100, 826 mm²	GV100, 815 mm²
Transistor Count	54.2 billion	54.2 billion	21.1 billion
Thermal Design Power	400 W	400 W	300 or 350 W
Lithography	7nm TSMC	7nm TSMC	12nm TSMC
Interface	SXM4	SXM4	SXM2 or SXM3
Architecture	Ampere	Ampere	Volta

 

Servers with Nvidia A100 80GB GPUs are likely to be very expensive. However, companies engaged in serious work on artificial intelligence and intensive computational tasks are likely to invest significantly in servers equipped with Nvidia's latest graphics processors, provided they have sufficient financial resources. The increased graphics memory of the A100 chip allows researchers to enhance the complexity of their AI models and address challenges that the 40GB model faced.

 

Applications of Nvidia A100 80GB Graphics Processor

The Nvidia A100 80GB graphics processor is one of the most advanced GPUs available, playing a crucial role in industry, science, and new technologies. Due to its high processing power and extensive memory, it is utilized across various fields. Here are the applications and impacts of this GPU in different industries:

1. Artificial Intelligence (AI) and Deep Learning:

   The A100 80GB is used in training AI and deep learning models that require processing large and complex datasets. It significantly enhances the performance of AI models, reduces training time, and improves the accuracy of algorithms.

 

2. Data Science and Big Data Analytics:

   For analyzing and processing large volumes of data in data centers and large organizations, this processor boosts data processing speed and enables the execution of complex data-driven models in parallel.

3. Scientific Computing:

   In scientific simulations, such as molecular simulations, climate modeling, and physics simulations, the A100 plays a vital role in increasing the accuracy and speed of scientific computations requiring high computational power.

4. Data Centers:

   A100 GPUs are used as the backbone of cloud data centers for GPU-based processing, such as AWS and Google Cloud services. They significantly reduce costs and enhance efficiency in large-scale data centers.

5. Cloud Computing:

   This GPU model supports scalable cloud services for companies and organizations needing powerful computing resources, enabling parallel processing and better utilization of hardware resources.

6. Graphics and Virtual/Augmented Reality:

   The A100 can be utilized in creating advanced simulations for games, virtual reality (VR), and augmented reality (AR), resulting in more realistic graphics and richer simulations for users.

7. Video Processing:

   For processing and producing high-resolution video content, such as 8K and beyond, the A100 enhances video processing quality and reduces rendering time for complex images.

8. Medical Research and Bioinformatics:

   In simulating complex medical and biological models and processing genetic data, the A100 accelerates medical research and drug discovery, improving the development of new medications.

9. Autonomous Systems:

   Typically used in autonomous vehicles and intelligent robots, the A100 helps in processing environmental data and real-time decision-making, improving accuracy in environment recognition, decision-making, and reducing errors in autonomous systems.

The Nvidia A100 80GB, with its high processing power and Tesla (Ampere) architecture, can drive significant advancements in various technological and scientific fields, from AI model training to scientific simulations and complex data processing.

 

Compatible Servers and Storage with Nvidia A100 80GB Graphics Processor

The Nvidia A100 80GB graphics processor is compatible with many advanced servers and storage systems. Due to its high computational requirements and specific architectural design for data centers and computational infrastructures, it is compatible with leading brands in server and storage solutions. Here are the recommended brands and platforms suitable for using the Nvidia A100:

Servers Compatible with Nvidia A100:

1. Dell EMC:

   Dell PowerEdge servers, particularly the R series (such as R760 and R750), are designed for use with Nvidia A100 GPUs. These Dell EMC servers are recommended for large data centers and organizations that require heavy processing and advanced computations. Dell EMC supports robust management software and scalable structures.

 

2. Hewlett Packard Enterprise (HPE):

HPE ProLiant and HPE Apollo servers are particularly suited for heavy AI and HPC (High-Performance Computing) tasks when equipped with A100 GPUs. HPE is recommended for enterprise environments that require high processing power and reliable systems. The Apollo servers, in particular, are designed for high-performance computing and efficient use of graphics processors.

 

3. Lenovo:

Lenovo ThinkSystem servers, such as the SR670 and SR950, are well-suited for utilizing Nvidia A100 GPUs. This brand is recommended for modern data centers due to its strong support for parallel processing and scalability. Lenovo servers are especially effective in HPC (High-Performance Computing) and AI applications.

4. Supermicro:

Supermicro supports architectures optimized for advanced GPUs like the Nvidia A100. SuperServer models are particularly recommended for AI and deep learning applications. Supermicro is suitable for organizations requiring high performance and GPU processing support, known for its modular and specialized systems for parallel processing.

5. Nvidia DGX Systems:

One of the most unique systems available, Nvidia DGX systems (such as the DGX A100), are specifically designed and optimized for Nvidia A100 GPUs. These systems are highly recommended for organizations and research centers needing immense computational power in AI and scientific computing. DGX Systems can utilize multiple A100 GPUs simultaneously, offering high performance for complex model executions.

 

Storage Solutions Compatible with Nvidia A100:

1. EMC PowerScale & PowerStore:

   Dell EMC’s PowerScale and PowerStore storage solutions are compatible with Nvidia A100 GPUs, making them ideal for large-scale and data-intensive processing. These storage systems are suitable for environments that require fast and scalable storage. PowerStore, in particular, is recommended for AI data center use.

2. HPE Nimble & HPE 3PAR:

   HPE storage systems, such as Nimble and 3PAR, are compatible with Nvidia A100 for handling large-scale data analysis and processing. These storage solutions are recommended for organizations needing fast and intelligent storage, especially in AI and HPC environments due to their excellent performance.

3. NetApp ONTAP AI:

   NetApp offers storage systems optimized for AI, such as ONTAP AI, which are compatible with Nvidia A100 GPUs. NetApp is recommended for organizations requiring storage and processing of large and complex datasets in AI and machine learning environments.

Conclusion:

- Dell EMC and HPE are top choices for utilizing Nvidia A100 due to their extensive product range and support for heavy computational environments.

- Lenovo and Supermicro are also excellent options for AI and HPC projects, especially for organizations needing parallel processing and scalability.

- For storage, Dell EMC PowerStore and HPE 3PAR are highly recommended due to their compatibility with AI processing and outstanding performance.

Choosing the right brand and system depends on your specific needs regarding processing, storage, and scalability. For precise information and advice, please contact us to identify the best solutions and most effective paths for your requirements.