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NVIDIA announced the Ampere-based A100 80GB data-center GPU at SC20 on November 16, 2020. It doubled the A100’s HBM capacity from 40GB to 80GB using HBM2e memory and delivered more than 2 TB/s of peak memory bandwidth, depending on the configuration. That bandwidth can feed data-intensive AI, analytics and scientific-computing workloads quickly, but it is not a universal application-speed guarantee.
What NVIDIA announced
The A100 80GB was introduced as an upgrade for NVIDIA’s HGX AI supercomputing platform and other server systems. Its defining change was capacity: 80GB of HBM2e GPU memory, twice the 40GB capacity of the earlier A100 model. NVIDIA also described bandwidth above 2 TB/s, allowing supported workloads to move large data sets between the GPU’s memory and compute resources at very high rates.
This is a data-center accelerator, not a consumer desktop gaming card. NVIDIA positioned it for artificial intelligence, data analytics and high-performance computing in servers and supercomputers.
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What “2 TB/s” of memory bandwidth means
Memory bandwidth is the theoretical rate at which a processor can read from and write to its attached memory. A bandwidth figure near 2 TB/s means the GPU can potentially transfer roughly two trillion bytes per second under suitable conditions. It is a measure of memory throughput, not a direct promise that every program will run twice as fast.
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- Data Center Class Reliability: Designed for 24x7 data center operations, ensuring optimum performance, durability, and longevity to meet demanding real-world conditions in machine learning and AI tasks.
- Ampere Architecture: Employs the world's most powerful data center GPU, offering exceptional AI, data analytics, and high-performance computing capabilities.
- Enhanced Tensor Cores: Accelerate deep learning matrix arithmetic at the heart of neural network training and inferencing, resulting in faster and more efficient AI computations.
- High-Speed HBM2e Memory: Equipped with 80GB of high-bandwidth memory, delivering improved raw bandwidth and higher memory bandwidth efficiency for data-intensive AI applications.
- PCIe Gen 4 Support: Provides double the bandwidth of PCIe Gen 3, improving data-transfer speeds for AI and data science workloads, maximizing performance for machine learning tasks.
Applications benefit most when they repeatedly stream large amounts of data and are limited by memory movement. Software that is constrained by arithmetic, synchronization, CPU work, storage or network transfers may see much smaller gains. Actual results also depend on the model, kernels, precision, batch size, software stack and complete server design.
A100 80GB PCIe versus SXM
NVIDIA publishes separate specifications for the PCIe and SXM versions. Both provide 80GB of HBM2e and support up to seven 10GB Multi-Instance GPU (MIG) partitions, but they are different server components with different power and integration requirements.
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- Discrete graphics card memory 40 GB
- Memory bandwidth (max) 1555 GB/s
- Graphics processor family NVIDIA
- Graphics processor A100
| Specification | A100 80GB PCIe | A100 80GB SXM |
|---|---|---|
| GPU memory | 80GB HBM2e | 80GB HBM2e |
| Published memory bandwidth | 1,935 GB/s | 2,039 GB/s |
| Standard listed TDP | 300W | 400W |
| Form factor | PCIe; dual-slot air-cooled or single-slot liquid-cooled | SXM module |
| MIG partitioning | Up to seven instances at 10GB each | Up to seven instances at 10GB each |
| Deployment | Requires a compatible PCIe server and its supported interconnect configuration | Requires a compatible SXM server platform and its supported interconnect configuration |
The rounded “2 TB/s” description therefore needs a qualifier. The PCIe model is listed at 1,935 GB/s, while the SXM model reaches 2,039 GB/s. Neither module should be treated as a drop-in replacement for the other: the connector, cooling, power delivery, interconnects and host-server design differ.
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Capacity and MIG: why 80GB matters
More memory lets a model, data set or simulation keep more working data on the GPU instead of transferring it to slower system memory or dividing it across additional GPUs. That can simplify deployment and reduce communication overhead when the workload fits within 80GB.
Rank #3
- PERFORMANCE: Features 6,912 CUDA cores and 432 third-gen Tensor cores delivering up to 19.5 TFLOPS FP32 performance for demanding AI and compute workloads
- MEMORY SPECIFICATIONS: Equipped with 80GB of HBM2e memory on a 5120-bit bus, providing massive 2,039 GB/s bandwidth
- ARCHITECTURE: Built on NVIDIA Ampere GA100 architecture with 40MB L2 cache and clock speeds of 1,275 MHz base to 1,410 MHz boost
- CONNECTIVITY: Features NVLink technology with 600 GB/s bandwidth for high-speed multi-GPU communication
- FORM FACTOR: SXM4 module design with 400W TDP, supporting up to 7 MIG partitions for workload optimization
MIG allows one A100 to be partitioned into isolated GPU instances. NVIDIA lists up to seven 10GB instances for both 80GB variants. This can let several users or services share one accelerator with dedicated portions of compute and memory, although the useful partition layout depends on the application and orchestration environment.
NVIDIA’s reported performance examples
The announcement included workload-specific results rather than a general benchmark covering all applications:
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- NVIDIA Ampere Architecture-based CUDA Cores - Double-speed processing for single-precision floating point (FP32) operations and improved power efficiency provide significant performance improvements for graphics and simulation workflows, such as complex 3D computer-aided design (CAD) and computer-aided engineering (CAE), on the desktop.
- Second-Generation RT Cores - With up to 2X the throughput over the previous generation and the ability to concurrently run ray tracing with either shading or denoising capabilities, second-generation RT Cores deliver massive speedups for workloads like photorealistic rendering of movie content, architectural design evaluations, and virtual prototyping of product designs. This technology also speeds up the rendering of ray-traced motion blur for faster results with greater visual accuracy.
- Third-Generation Tensor Cores - New Tensor Float 32 (TF32) precision provides up to 5X the training throughput over the previous generation to accelerate AI and data science model training without requiring any code changes. Hardware support for structural sparsity doubles the throughput for inferencing. Tensor Cores also bring AI to graphics with capabilities like DLSS, AI denoising, and enhanced editing for select applications.
- Third-Generation NVIDIA NVLink - Increased GPU-to-GPU interconnect bandwidth provides a single scalable memory to accelerate graphics and compute workloads and tackle larger datasets.
- 48 Gigabytes (GB) of GPU Memory - Ultra-fast GDDR6 memory, scalable up to 96 GB with NVLink, gives data scientists, engineers, and creative professionals the large memory necessary to work with massive datasets and workloads like data science and simulation.
- RNN-T automatic speech recognition: NVIDIA reported 1.25× higher inference throughput from a single A100 80GB MIG instance in its production comparison.
- Retail analytics: On a terabyte-size big-data analytics benchmark, NVIDIA reported performance gains of up to 2×.
- Quantum Espresso: NVIDIA reported nearly 2× throughput gains on a single node for this materials-simulation workload.
These are NVIDIA-reported claims tied to the named workloads and comparison conditions. They should not be read as expected speedups for every neural network, database, simulation or server configuration, and they are not independent test results.
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Power, form factor and procurement considerations
The published 300W PCIe and 400W standard SXM power figures affect chassis design, cooling, power supplies and operating cost. The SXM module is intended for purpose-built server platforms, while the PCIe version fits a compatible expansion-card design. NVIDIA also describes NVLink and PCIe interconnect options and specific server configurations, so a buyer must validate the complete GPU, baseboard, interconnect, cooling and firmware combination before ordering.
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- 24GB Video Memory
- Fourth Generation Tensor Cores
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NVIDIA’s 2020 announcement said Atos, Dell Technologies, Fujitsu, GIGABYTE, Hewlett Packard Enterprise, Inspur, Lenovo, Quanta and Supermicro were expected to offer systems using HGX A100 baseboards in four- or eight-GPU configurations during the first half of 2021. That was a historical delivery expectation, not confirmation of current stock, pricing or an active system-provider program.
Who benefits from the A100 80GB?
- Large AI models and inference services: 80GB can keep larger models or batches resident on one GPU, while MIG can divide the device among services.
- Data analytics: Memory bandwidth and capacity can help workloads that scan large in-memory data sets.
- Scientific and engineering simulations: Memory-intensive solvers may benefit when their kernels are optimized for the A100 architecture.
- Server and supercomputing operators: SXM systems can provide tightly integrated multi-GPU designs, while PCIe cards suit appropriately designed servers.
For a small desktop, gaming PC or general-purpose workstation, the A100’s server-only integration, power and software requirements make it a fundamentally different proposition from a consumer GPU.
How to interpret the headline specification
- Check whether the system uses the PCIe or SXM A100 80GB variant.
- Use the exact published bandwidth—1,935 GB/s for PCIe or 2,039 GB/s for SXM—instead of assuming every A100 80GB card delivers the same number.
- Confirm that the workload is memory-bandwidth- or memory-capacity-limited.
- Verify server cooling, power, interconnect, firmware and software compatibility.
- Treat vendor performance examples as workload-specific evidence, not universal benchmarks.
The Bottom Line
The A100 80GB’s headline achievement was combining 80GB of HBM2e with roughly 2 TB/s of memory bandwidth for data-center AI, analytics and HPC. The exact figure and deployment requirements depend on PCIe versus SXM, and application performance depends on the workload and the rest of the server.
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