# NVIDIA TESLA V100

> GPU, Graphics processing unit

**Wikidata**: [Q123054298](https://www.wikidata.org/wiki/Q123054298)  
**Source**: https://4ort.xyz/entity/nvidia-tesla-v100

## Summary
The NVIDIA TESLA V100 is a high-performance graphics processing unit (GPU) designed for artificial intelligence, machine learning, and high-performance computing applications. Developed by NVIDIA, it is part of the Tesla product line optimized for data center use rather than consumer gaming or graphics. Built on the Volta architecture, the V100 delivers significant computational power through its 5,120 CUDA cores and 16 GB or 32 GB of high-bandwidth memory (HBM2).

## Key Facts
- Manufacturer: NVIDIA
- Product Line: Tesla
- Architecture: Volta
- Release Year: 2017
- CUDA Cores: 5,120
- Memory Options: 16 GB or 32 GB HBM2
- Memory Bandwidth: Up to 900 GB/s
- Interconnect Technology: NVLink
- Designed For: AI training/inference, HPC, scientific simulations
- Subclass Of: GPU (Graphics Processing Unit)

## FAQs
### Q: What is the NVIDIA TESLA V100 used for?
A: The NVIDIA TESLA V100 is primarily used for artificial intelligence and deep learning workloads, including neural network training and inference. It also supports high-performance computing tasks such as scientific simulations, financial modeling, and data analytics in data centers.

### Q: How much memory does the NVIDIA TESLA V100 have?
A: The NVIDIA TESLA V100 comes with either 16 GB or 32 GB of high-bandwidth memory (HBM2), depending on the model. This allows it to handle large datasets efficiently during compute-intensive operations like AI training.

### Q: Is the NVIDIA TESLA V100 suitable for gaming?
A: No, the NVIDIA TESLA V100 is not intended for gaming. It lacks display outputs and is specifically engineered for server and workstation environments focused on compute performance rather than rendering visuals for end users.

## Why It Matters
The NVIDIA TESLA V100 marked a major advancement in GPU-accelerated computing when released in 2017. As one of the first GPUs built on the Volta architecture, it introduced Tensor Cores—specialized units optimized for mixed-precision matrix operations critical to deep learning. These innovations significantly accelerated AI research and deployment across industries, enabling faster training times for complex models. Its adoption in supercomputers and cloud platforms helped establish new benchmarks for scalable AI infrastructure. By delivering unprecedented levels of parallel processing power, the V100 played a pivotal role in democratizing access to advanced machine learning capabilities and pushing forward modern AI development pipelines.

## Notable For
- First GPU to feature dedicated Tensor Cores for accelerating AI computations
- Utilization of the Volta microarchitecture, offering enhanced performance per watt over previous generations
- Support for up to 32 GB of high-bandwidth memory (HBM2)
- Integration of NVLink technology for improved multi-GPU communication speeds
- Widespread deployment in top-tier data centers and supercomputing systems globally

## Body

### Overview
The NVIDIA TESLA V100 is a professional-grade GPU tailored for enterprise and scientific computing environments. Unlike GeForce series cards aimed at consumers, the V100 focuses exclusively on raw computational throughput and efficiency for non-rendering tasks.

### Technical Specifications
- **Architecture**: Volta GV100
- **Process Node**: 12 nm FinFET (manufactured by TSMC)
- **CUDA Cores**: 5,120
- **Tensor Cores**: 640 (for mixed-precision AI calculations)
- **FP32 Performance**: ~15 TFLOPS
- **FP64 Performance**: ~7.8 TFLOPS
- **Memory Configurations**:
  - 16 GB HBM2 with 4,096-bit bus width
  - 32 GB HBM2 with 4,096-bit bus width
- **Memory Bandwidth**: Up to 900 GB/s
- **Interconnect**: NVLink 2.0 (up to 300 GB/s bi-directional bandwidth between GPUs)
- **TDP**: 250W or 300W depending on cooling solution

### Design Purpose
The TESLA V100 was developed to meet growing demands in artificial intelligence, particularly in training large-scale neural networks. Its inclusion of Tensor Cores enables efficient execution of reduced precision arithmetic commonly found in deep learning algorithms without sacrificing accuracy.

### Deployment Context
This GPU has been widely adopted in hyperscale data centers and supercomputing clusters due to its scalability and energy efficiency. Major cloud providers integrate V100-based instances into their offerings for machine learning services, while research institutions utilize them for computationally heavy simulations in physics, chemistry, and biology.

### Legacy Impact
As part of the Tesla brand evolution, the V100 set foundational standards later built upon by subsequent architectures like Turing and Ampere. Its influence extends beyond hardware—it shaped software frameworks and optimization practices that continue to define modern GPU programming paradigms.