Interconnect: PCIe Gen4
Software Stack: CUDA, TensorRT, NVIDIA AI Enterprise, NVIDIA vGPU
Organizations that commonly deploy A40 GPUs include:
The GPU is especially attractive for buyers seeking a versatile accelerator capable of supporting multiple enterprise workloads.
The A40 is widely available through OEM server vendors and enterprise infrastructure providers. It is commonly deployed in environments that require GPU virtualization or a combination of AI and graphics workloads.
Because it has been on the market for several years, the A40 is often available at competitive pricing in enterprise GPU marketplaces.
The NVIDIA A40 is a data center GPU built on the Ampere architecture, designed to support a combination of AI acceleration, professional visualization, and virtual workstation workloads. It provides a flexible compute platform for enterprise environments that require GPU acceleration across multiple types of workloads.
The accelerator includes second-generation Tensor Cores, enabling acceleration of deep learning inference tasks and machine learning workflows. With 48 GB of GDDR6 memory, the A40 can support larger datasets and complex models compared to many earlier data center GPUs.
Unlike many training-focused accelerators, the A40 emphasizes versatility, making it suitable for AI inference, 3D rendering, engineering simulations, and GPU-accelerated virtualization environments.
| Specification | Value |
|---|---|
| Architecture | NVIDIA Ampere |
| CUDA Cores | 10,752 |
| Tensor Cores | 336 (2nd Gen) |
| Memory | 48 GB GDDR6 |
| Memory Bandwidth | ~696 GB/s |
| Interconnect | PCIe Gen4 |
| Form Factor | PCIe |
| Max TGP | ~300 W |
| Precision Support | FP64, FP32, TF32, FP16, BF16, INT8 |
| Typical AI Compute | ~150 TFLOPS (FP16 Tensor) |
| Process Node | Samsung 8 nm |
| Transistor Count | ~28 Billion |
| MIG Support | Not Supported |
| NVLink (Peer) | Supported (2-GPU bridge) |
Compared with newer GPUs such as the L40S, the A40 delivers lower AI performance but remains widely deployed due to its versatility and enterprise software ecosystem.
The GPU is particularly suited for organizations requiring GPU acceleration across multiple enterprise workloads.
Comparable NVIDIA GPUs:
Competitor Accelerators:
These alternatives provide different tradeoffs between AI performance, memory capacity, and enterprise features.
Related NVIDIA GPUs:
Complementary Infrastructure:
The NVIDIA A40 is a versatile Ampere-based data center GPU designed to support a wide range of enterprise workloads, including AI inference, professional visualization, and virtual workstation environments. With 48 GB of GDDR6 memory and Tensor Core acceleration, it provides a flexible platform for organizations deploying GPU-accelerated infrastructure.
Although newer architectures offer improved AI performance, the A40 remains widely used due to its balance of compute capability, memory capacity, and enterprise virtualization support.
The next important GPU to add is NVIDIA T4.
Why this one matters:
Below is the page written in the same structure as your other GPU pages.
