VRAM
Video RAM is the memory on a graphics card used for processing visual data and, in the context of AI, for running large language models.
What is VRAM?
VRAM (Video Random Access Memory) is the working memory of a graphics processing unit (GPU). While VRAM was originally used for rendering graphics and games, it now plays a crucial role in artificial intelligence. Large language models (LLMs) require enormous amounts of VRAM to function: the more VRAM available, the larger and more powerful models that can be run.
How does VRAM work?
VRAM provides a GPU with fast access to the data it needs for computations. In AI applications, the parameters (weights) of a language model are loaded into VRAM. While processing a request, the GPU reads these parameters at lightning speed and performs millions of calculations in parallel. The more VRAM available, the more model parameters can fit in memory simultaneously. This directly determines which models can be run and how large the context window can be.
Example
Wabber runs advanced language models on its private cluster with 128GB of VRAM, distributed across multiple professional GPUs. A client in the healthcare sector wants an AI assistant that can analyze and summarize extensive patient records. Thanks to the large VRAM capacity, Wabber can run a model with a context window of tens of thousands of tokens, allowing complete records to be processed at once. With less VRAM, the model could only process small fragments, which would reduce the quality of the summary.
Why is VRAM important?
For businesses considering AI deployment, VRAM capacity is one of the most important technical considerations. Insufficient VRAM leads to slower processing or the necessity to use smaller, less capable models. By utilizing the Wabber cluster, you benefit from professional VRAM capacity without having to invest in expensive GPU hardware yourself. Moreover, all processing takes place on our own hardware in the Netherlands, guaranteeing full control over data processing and privacy.
Related solutions
Frequently asked questions
How much VRAM do you need to run an AI model?
The required amount of VRAM depends on the model size. Small models (7B parameters) require approximately 14-16GB of VRAM, while larger models (70B+ parameters) quickly need 80GB or more. Wabber has 128GB of VRAM, allowing us to run most professional models without compromising on capacity or speed.
What is the difference between VRAM and regular RAM?
Regular RAM (system memory) is used by the processor (CPU) for general tasks. VRAM is located on the graphics card (GPU) and is optimized for executing enormous amounts of calculations in parallel. For AI workloads, VRAM is essential because GPUs, thanks to their parallel architecture, are much faster than CPUs at processing the mathematical operations required by language models.
Do I need to purchase my own VRAM for AI applications?
No, that is not necessary. Wabber offers access to a private cluster with 128GB of VRAM, so you can benefit from professional AI capacity without investing in expensive GPU hardware yourself. Your data is processed locally on Wabber's own hardware in the Netherlands, guaranteeing privacy and data sovereignty.
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