Released in 2020, the NVIDIA A100 is a data center GPU built on NVIDIA's Ampere architecture. Designed from the ground up for high-performance computing and AI workloads, it's one of the most capable GPUs for training and inference at scale.
Understanding the full set of NVIDIA A100 GPU specifications helps you determine whether it is the right hardware for your use case.
A100 Form Factors
The A100 is available in two distinct form factors: PCIe and SXM. Each one targets different deployment environments, and the choice between them directly affects thermal design, bandwidth, and peak performance.
A100 PCIe
The PCIe variant fits into standard server motherboards using a PCIe 4.0 x16 interface. It comes in both 40GB HBM2 and 80GB HBM2e memory configurations and draws up to 300W through the PCIe slot and auxiliary power connectors.
Because it uses passive cooling and relies on server chassis airflow, it integrates easily into a wide range of existing infrastructure.
A100 SXM
The SXM4 variant mounts directly to NVIDIA's NVLink Switch System baseboard, enabling tighter GPU-to-GPU interconnects and a higher power envelope of 400W.
This higher thermal headroom translates directly into improved clock speeds and memory bandwidth compared to the PCIe version. The SXM form factor is the basis for NVIDIA's own DGX and HGX systems, making it the preferred choice for maximum multi-GPU throughput.
A100 Specifications
The table below shows key NVIDIA A100 specs across form factors and memory configurations.
| Specification | A100 PCIe 40GB | A100 PCIe 80GB | A100 SXM 40GB | A100 SXM 80GB |
|---|---|---|---|---|
| Architecture | Ampere | |||
| GPU | GA100 | |||
| CUDA Cores | 6,912 | |||
| Tensor Cores (3rd Gen) | 432 | |||
| GPU Boost Clock | 1,410 MHz | |||
| A100 VRAM | 40GB HBM2 | 80GB HBM2e | 40GB HBM2 | 80GB HBM2e |
| Memory Bandwidth | 1,555GB/s | 1,935GB/s | 1,555GB/s | 2,039GB/s |
| Memory Bus Width | 5,120-bit | |||
| L2 Cache | 40 MB | |||
| TDP | 250 W | 300 W | 400 W | |
| Interconnect | PCIe 4.0 x16 | NVLink 3.0 (600GB/s) | ||
| PCIe Bandwidth | 64GB/s | |||
| Multi-Instance GPU (MIG) | Yes (up to 7) | |||
The 80GB SXM variant delivers the highest memory bandwidth and capacity of any A100 configuration, making it the preferred choice for large model training runs where the working dataset does not fit in smaller GPU memory.
A100 Supported Precisions
An important part of NVIDIA A100 GPU specifications for AI is the range of precisions supported through its CUDA and Tensor Cores. Different precisions offer different trade-offs between numerical accuracy and raw throughput.
| Precision | A100 | Notes |
|---|---|---|
| FP64 (Tensor Core) | 19.5 TFLOPS | Scientific computing and HPC |
| FP64 (CUDA Core) | 9.7 TFLOPS | Standard double precision |
| FP32 | 19.5 TFLOPS | Full single precision |
| TF32 (Tensor Core) | 156 TFLOPS / 312 TFLOPS* | Default precision for DL frameworks |
| FP16 | 312 TFLOPS / 624 TFLOPS* | Standard half precision for training |
| BFLOAT16 (Tensor Core) | 312 TFLOPS / 624 TFLOPS* | Favored for LLM training stability |
| INT8 (Tensor Core) | 624 TOPS / 1,248 TOPS* | Quantized inference |
| INT4 (Tensor Core) | 1,248 TOPS / 2,496 TOPS* | Highly quantized inference |
| * Values with sparsity enabled via NVIDIA's Sparse Tensor Core feature. | ||
The A100 does not natively support FP8 computation, which was introduced with the Hopper generation (H100) and its fourth-generation Tensor Cores.
A100 Chip
The A100 is built on the GA100 die, manufactured by TSMC on a 7 nm process node. The full die contains 54 billion transistors in a 826 mm² package, though the shipping GPU enables 108 of a possible 128 Streaming Multiprocessors (SMs) to maintain yield.
Each SM houses 64 FP32 CUDA cores and four third-generation Tensor Cores, with dedicated shared memory and L1 cache per SM totaling 192 KB per SM. A unified 40 MB L2 cache serves all SMs and helps reduce pressure on the HBM2e memory subsystem.
The chip introduced third-generation Tensor Cores capable of executing a new TF32 format that provides the numerical range of FP32 with roughly the throughput of FP16, easing migration of existing FP32 training pipelines without code changes.
It also introduced Multi-Instance GPU (MIG) technology, which partitions a single physical GPU into up to seven isolated GPU instances, each with dedicated memory bandwidth, cache, and CUDA cores. MIG makes it practical to serve multiple independent inference workloads on a single card without contention.
A100 Systems
NVIDIA packaged the A100 into purpose-built multi-GPU systems that take full advantage of the SXM form factor and NVLink interconnects.
DGX A100
The NVIDIA DGX Station A100 and the DGX A100 server are NVIDIA's reference AI compute platforms built around eight A100 SXM GPUs. Each DGX A100 system provides 320GB of aggregate GPU memory in the 40GB configuration or 640GB in the 80GB configuration, connected through a full NVLink mesh delivering 600GB/s of bidirectional bandwidth between every GPU pair.
The system includes dual AMD EPYC Rome CPUs, 1 TB of system memory, and high-speed NVMe storage, making it a self-contained training node. The DGX Station A100 is the workstation variant of this design, intended for on-premises deployment where rack-mounted servers are not practical.
HGX A100
The HGX A100 is NVIDIA's OEM compute baseboard reference design, which third-party server manufacturers use to build their own A100-based systems.
Like the DGX, HGX configurations support four or eight A100 SXM GPUs connected via NVLink, but the baseboard is integrated into standard server chassis from vendors such as Dell, HPE, and Supermicro. This makes the HGX A100 the underlying hardware behind most cloud provider A100 instances, including those available on Thunder Compute.
Running A100 Workloads on Thunder Compute
The A100 continues to be a strong choice for AI training, fine-tuning, and inference, particularly for mid-sized models and batch workloads where the 80GB memory tier provides ample capacity without the cost premium of newer hardware. A100 cloud pricing has become increasingly accessible as the GPU fleet has expanded across providers.
Thunder Compute offers on-demand access to A100 GPUs without long-term commitments, so you can run a large training job today and scale down when it completes. Try Thunder Compute GPUs and get your first workload running in minutes.