The GPU is a specialized processor optimized for parallel computations, which are common in AI tasks. Just as the CPU needs quick access to data stored in system RAM, the GPU requires its own high-speed memory. This dedicated memory is called Video RAM, or VRAM.
Think of VRAM as memory built directly onto the graphics card, sitting right next to the GPU cores. This physical proximity is important. System RAM, while large, is farther away from the GPU, and accessing it is much slower for the GPU compared to accessing its own VRAM.
VRAM stands for Video Random Access Memory. It's a type of RAM specifically designed to work with a Graphics Processing Unit (GPU). Its primary job is to store data that the GPU needs to access quickly. In traditional graphics applications, this includes things like textures, frame buffers, and complex 3D model data. For AI and specifically Large Language Models, VRAM takes on a new, significant role.
Relationship between CPU, System RAM, GPU, and VRAM. VRAM provides fast, dedicated memory located directly on the graphics card for the GPU.
Large Language Models, as we learned in Chapter 1, are defined by their parameters. These parameters represent the learned knowledge of the model. When you want to use an LLM (a process called inference), these parameters need to be loaded into memory where the processor can access them.
Since GPUs are exceptionally good at the types of calculations needed for LLMs, we want the GPU to do the heavy lifting. For the GPU to work efficiently, the model's parameters need to be stored in its local, high-speed memory: the VRAM.
Imagine a chef (the GPU) needing ingredients (model parameters) to cook a complex dish (generate text). VRAM is like a dedicated, well-organized prep station right next to the chef, holding all the immediately needed ingredients. System RAM is like the main pantry down the hall. While the pantry holds more overall, constantly running back and forth slows down the cooking process considerably. If the required ingredients don't fit on the prep station (VRAM), the chef's work becomes inefficient.
The most discussed specification of VRAM is its capacity, usually measured in gigabytes (GB). This capacity directly limits the size of the LLM that can be run efficiently on that GPU.
Here's the basic connection:
If an LLM requires, for example, 14 GB of memory for its parameters, but your GPU only has 8 GB of VRAM, you generally cannot load the entire model onto the GPU directly. While there are techniques to manage this (like splitting the model or using system RAM), they significantly reduce performance because the GPU is constantly waiting for data to be transferred from slower memory locations.
Therefore, the amount of VRAM available on a GPU is often the primary hardware constraint when deciding if you can run a specific LLM.
Besides capacity, the speed at which data can be moved between the VRAM and the GPU cores is also important. This is called memory bandwidth, typically measured in gigabytes per second (GB/s).
Higher memory bandwidth allows the GPU cores to be fed data more quickly, preventing them from sitting idle waiting for the next piece of information (like parameters or intermediate calculations). For LLMs, where enormous amounts of data (parameters) are constantly being accessed, high VRAM bandwidth contributes significantly to faster response times (lower latency) during inference.
Consumer-grade GPUs typically have lower VRAM capacity and bandwidth compared to professional or data-center GPUs designed for AI workloads.
In summary, VRAM is the GPU's dedicated, high-speed memory. Its capacity is a primary factor determining which LLMs can be run efficiently, as the model's parameters must fit into this space for optimal performance. Its bandwidth influences how quickly the GPU can process these parameters. Understanding VRAM is essential as we move towards estimating the hardware needed for different LLM sizes.
Was this section helpful?
© 2026 ApX Machine LearningEngineered with