VRAM
GPU Hardware & ComputeVideo Random Access Memory (VRAM) is the dedicated high-speed memory used by a Graphics Processing Unit (GPU) to store and access data required for computational tasks. In data center GPUs it is usually called GPU memory, and it is built on high-bandwidth memory (HBM). In artificial intelligence, VRAM holds model parameters, intermediate calculations, and other data needed during training and inference, allowing GPUs to process complex workloads efficiently.
Each NVIDIA B300 GPU in Eleveight AI's AI factory has 288 GB of HBM3e memory with up to 8 TB/s of bandwidth, which leaves room for large models and long contexts on a single GPU.
Overview
VRAM is a specialized type of memory directly accessible to a GPU, designed to provide the high data throughput required for parallel processing. Unlike system RAM, which is primarily used by the CPU, VRAM stores information that GPUs need to access rapidly during computation. In artificial intelligence, VRAM is essential for storing model weights, input data, activations, and intermediate results generated during neural network operations.
The amount of available VRAM determines which models and workloads can fit within a GPU's memory capacity. Large language models containing billions of parameters can require substantial memory, sometimes exceeding the capacity of a single GPU. Modern AI accelerators use high-bandwidth memory (HBM) technologies to provide the memory capacity and bandwidth required for increasingly demanding AI workloads.
How it works
When an AI workload is executed, the GPU loads the required model parameters and computational data into its memory. During inference, VRAM stores model weights, temporary activations, and the key-value (KV) cache used by transformer-based language models to retain information from previously processed tokens. As the model processes input tokens and generates responses, the GPU repeatedly accesses data stored in VRAM to perform mathematical operations. During training, additional memory is required for activations, gradients, optimizer states, and other intermediate data involved in updating model parameters.
VRAM performance depends on two major characteristics: memory capacity and memory bandwidth. Memory capacity determines how much data can be stored on the GPU at once, while memory bandwidth determines how quickly data can be transferred between GPU memory and its processing units.
When a model or workload exceeds the available VRAM, the system may need to distribute the workload across multiple GPUs, reduce memory requirements through techniques such as quantization, or offload data to slower memory.
Why it matters
VRAM is one of the most important hardware resources in AI computing because it directly influences model deployment capabilities, processing efficiency, and infrastructure scalability. Insufficient VRAM can prevent large AI models from running on a single GPU, requiring additional hardware or memory optimization techniques. High-capacity VRAM allows organizations to deploy larger models, support longer context windows, and accommodate larger inference batches, provided sufficient memory is available for the associated workloads.
High memory bandwidth is equally important, particularly for large language model inference, where repeatedly accessing model weights and cached information can become a significant performance bottleneck. Understanding VRAM requirements is essential for selecting appropriate GPU hardware, estimating infrastructure costs, and optimizing AI model performance.
Use cases
Use cases
- Running large language models with billions of parameters
- Storing model weights and intermediate computations
- Supporting long-context AI inference through KV caching
- Training deep learning and generative AI models
- Increasing inference batch sizes and throughput • Distributing memory-intensive workloads across multiple GPUs
- Selecting GPU infrastructure based on model memory requirements