> ## Documentation Index
> Fetch the complete documentation index at: https://docs.visent.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Glossary

> Common terminology and definitions for GPU monitoring, pricing, and benchmarking

# Glossary

Common terms and definitions used throughout Visent documentation and the GPU observability ecosystem. Understanding these terms will help you navigate GPU monitoring, pricing analysis, and performance benchmarking concepts.

## GPU Terms

**CUDA Cores**\
Parallel processing units in NVIDIA GPUs that execute instructions. More CUDA cores generally mean higher parallel processing capability.

**FLOPS (Floating Point Operations Per Second)**\
A measure of computational performance, indicating how many floating-point calculations a GPU can perform per second.

**GPU Memory (VRAM)**\
Dedicated memory on the GPU used for storing data, textures, and computational results. Also called video memory or graphics memory.

**GPU Utilization**\
The percentage of time the GPU is actively processing workloads, indicating how efficiently the GPU resources are being used.

**Memory Bandwidth**\
The rate at which data can be read from or written to GPU memory, measured in GB/s.

**Tensor Cores**\
Specialized processing units in modern NVIDIA GPUs designed for AI and machine learning workloads, providing accelerated matrix operations.

**TGP (Total Graphics Power)**\
The maximum power consumption of a GPU under full load, measured in watts.

## Performance Metrics

**Batch Size**\
The number of samples processed simultaneously in machine learning workloads. Larger batch sizes can improve GPU utilization.

**Inference**\
The process of using a trained machine learning model to make predictions on new data.

**Latency**\
The time delay between input and output in a system, often measured in milliseconds for GPU workloads.

**Throughput**\
The number of operations, requests, or tasks completed per unit of time, typically measured as ops/sec.

**Training**\
The process of teaching a machine learning model using training data to learn patterns and make predictions.

## Cloud Computing

**Instance Type**\
A predefined configuration of CPU, memory, storage, and GPU resources offered by cloud providers.

**On-Demand Instance**\
Cloud instances that can be launched immediately and billed by the hour or second without long-term commitments.

**Reserved Instance**\
Cloud instances purchased for a specific term (1-3 years) at a discounted rate compared to on-demand pricing.

**Spot Instance**\
Cost-effective cloud instances that use spare capacity at significantly reduced prices, but can be interrupted.

## Monitoring Terms

**Agent**\
Software component installed on GPU nodes to collect and report performance metrics to monitoring systems.

**Alert Rule**\
Configured conditions that trigger notifications when metrics exceed thresholds or meet specific criteria.

**Dashboard**\
Graphical interface displaying real-time and historical metrics, charts, and system status information.

**Metric**\
A measurable value that represents system performance, resource usage, or operational status.

**Node**\
A physical or virtual machine in a cluster or infrastructure, typically referring to a system with GPU resources.

**Time Series**\
Data points collected over time, showing how metrics change and trend over different time periods.

## Pricing Terms

**Market Intelligence**\
Data and analysis about GPU pricing trends, availability, and competitive landscape across providers.

**Price Forecast**\
Predictive analysis of future GPU pricing based on historical data and market trends.

**Spot Price**\
Dynamic pricing for spare cloud capacity that fluctuates based on supply and demand.

**TCO (Total Cost of Ownership)**\
The complete cost of GPU infrastructure including hardware, software, maintenance, and operational expenses.

## Benchmarking

**Baseline**\
A reference performance measurement used for comparison with other configurations or optimizations.

**Benchmark Suite**\
A collection of standardized tests designed to measure different aspects of GPU performance.

**Performance Profile**\
A detailed analysis of how a system performs across different workloads and configurations.

**Regression Testing**\
Testing to ensure that performance hasn't degraded after changes to hardware or software configurations.

## API Terms

**Endpoint**\
A specific URL where an API can access the resources or data it needs to perform operations.

**Rate Limiting**\
Restrictions on the number of API requests that can be made within a specific time period.

**Webhook**\
HTTP callbacks triggered by specific events, allowing real-time notifications and integrations.

## Next Steps

* [Check the FAQ](faq) for common questions
* [Review the changelog](changelog) for recent updates
* [Explore API documentation](../api/authentication) for integration details