Interconnect: PCIe Gen4
Software Stack: CUDA, TensorRT, cuDNN, NVIDIA AI Enterprise
Organizations that typically deploy A10G GPUs include:
The A10G is particularly attractive for buyers seeking versatile GPU compute without the cost of high-end training accelerators.
The A10G is widely available across both enterprise server vendors and cloud GPU platforms. Many cloud providers use the GPU in machine learning and graphics-accelerated instances due to its balance of performance and flexibility.
Because of its popularity in cloud environments, the A10G remains a common option for organizations seeking cost-effective GPU acceleration for inference and compute workloads.
The NVIDIA A10G is a data center GPU built on the Ampere architecture, designed to provide a balance of AI inference performance, graphics capabilities, and power efficiency. It is commonly deployed in cloud environments and enterprise infrastructure for machine learning inference, rendering workloads, and mid-scale model training.
The GPU incorporates second-generation Tensor Cores that accelerate AI operations such as matrix multiplication and mixed-precision deep learning. Combined with 24 GB of GDDR6 memory, the A10G supports a wide range of AI workloads while maintaining relatively moderate power requirements compared to larger training accelerators.
Because of its flexible design, the A10G is widely used in environments that require both AI inference and GPU compute capabilities, including recommendation systems, image processing pipelines, and interactive AI services.
| Specification | Value |
|---|---|
| Architecture | NVIDIA Ampere |
| CUDA Cores | 9,216 |
| Tensor Cores | 288 (2nd Gen) |
| Memory | 24 GB GDDR6 |
| Memory Bandwidth | ~600 GB/s |
| Interconnect | PCIe Gen4 |
| Form Factor | PCIe |
| Max TGP | ~300 W |
| Precision Support | FP32, TF32, FP16, BF16, INT8 |
| Typical AI Compute | ~125 TFLOPS (FP16 Tensor) |
| Process Node | Samsung 8 nm |
| Transistor Count | ~28 Billion |
| MIG Support | Not Supported |
| NVLink (Peer) | Not Supported |
The A10G is often deployed where organizations require general-purpose GPU acceleration across both AI and graphics workloads.
Comparable NVIDIA GPUs:
Competitor Accelerators:
These alternatives provide different balances of performance, efficiency, and ecosystem support.
Related NVIDIA GPUs:
Complementary Infrastructure:
The NVIDIA A10G is a versatile Ampere-based data center GPU designed to balance AI inference performance, graphics capabilities, and cost efficiency. With 24 GB of GDDR6 memory and Tensor Core acceleration, it supports a wide range of machine learning and compute workloads.
Although newer GPUs offer higher efficiency or training performance, the A10G remains a widely deployed accelerator in cloud environments and enterprise infrastructure due to its flexibility and strong price-to-performance ratio.
The next most important chip to add after A100 → L4 → A10G is NVIDIA RTX 4090.
Even though it’s a consumer GPU, it has huge search traffic and real-world AI usage because:
Below is the page in the exact same structure as the others.
