How Much VRAM Does AI Need ?

As generative AI and LLMs (Large Language Models) continue to scale every quarter, what determines what is possible with AI is one simple hardware constraint: GPU Memory (VRAM). While most of the conversation around AI infrastructure is about compute power such as tensor cores, FLOPS, GPU architectures, energy and physical structures, the reality in this case is much simpler.

If the model size surpasses the VRAM of the GPU, the model will not run efficiently, or at all. Whether we are deploying inference workloads, fine-tuning models, or building full-scale AI infrastructures, sizing VRAM is one of the critical decisions to be made. This post will guide you on how VRAM is actually used for AI work and how to correctly size GPUs for real world LLM deployments.

What Uses VRAM in AI?

What are Model Weights ?

Model weights are seen as the parameters available on the model itself such as a 7B model which has approximately 7 Billion parameters. The usage of memory depends heavily on precision, and with the quantization technique, often larger model weights can be considered for less performant GPUs.

What is Quantization ?

Quantization is one powerful technique for reducing GPU memory (VRAM) usage in AI workloads. Meaning, instead of storing the model weights in high precision such as FP16 or FP32, quantization reduces them to lower precision formats such as INT8 or even 4-bit.

Precision	VRAM Usage	Example (7B Model)
FP16	100%	~14GB
INT8	~50%	~7GB
4-bit	~25%	~3.5GB

Why Does Quantization Matter ?

• Enables LLMs to run on smaller GPUs
• Reduces infrastructure cost dramatically
• Makes local and edge deployments possible

Trade-offs on Quantization

• Precision and accuracy loss which varies by model
• Workloads such as LLM training still require high precision.

A lot of companies who want to leverage AI and share it with all employees within the company have the option to set up their own AI infrastructure for security reasons rather than relying on web applications, terminal-based applications such as CLI or TUI-based programs from other enterprises to run AI for every employee. Hence having local AI infrastructure that supports quantization of large models will help with budget, as AI companies such as OpenAI, Alphabet, Anthropic among others tend to offer expensive and rate-limited LLMs. Using local AI with quantized free versions of certain LLMs such as GPT-OSS, Kimi K2.5, or QWEN 3 with higher parameters will still be helpful and often perform better than using high precision LLMs with fewer parameters.

Activations

• Intermediate data generated during forward passes
• Required for training and often for inference
• Scales with batch size and sequence length

Gradients (Training Purposes Only)

• Stored during backpropagation
• Roughly the same size as model weights, as it can be seen as a guide telling the model which way to adjust its parameters to reduce error

KV Cache (Critical for LLM Inference)

• Stores attention key/value pairs for tokens
• Grows with sequence length and number of requests (batch size)
• Longer Context Window = Significantly higher VRAM usage

Currently the best context window to be used on text-based AI workloads is 200,000 tokens, which allows a large enough workflow and retains memory of the entire conversation with lower chances of hallucination no matter what LLM is being used. Larger context windows can be achieved but the GPU requirements will also increase.

Inference vs Training: The Differences

Inference (Serving Models)

The process of using trained AI models to generate outputs. What happens during inference:

• The model loads into GPU memory
• Input data (prompt) is processed
• The model generates a response

Inference only uses VRAM for model weights, the model parameters that will be used for the matrix calculations, and KV cache which stores the attention data for previously processed tokens which grow with context length and number of users within the same batch.

Training / Fine-Tuning

The process of teaching a model by updating its weights (parameters) and reducing error levels with prompt processing. During training the model undergoes:

• Process input (forward pass)
• Calculates error (loss)
• Adjusts weights (backpropagation)

Training is much more memory intensive than inference mainly because it requires:

• Model weights
• Activations (stored for backpropagation)
• Gradients
• Optimizer states

When it comes to training the objective is to change the model for the better, which requires storing much more data in memory, as the model not only uses VRAM to load the model but also to save cached data from current running context windows. Hence having a 24GB VRAM GPU does not mean you can run a 24GB sized LLM, which is why techniques like quantization can help in running such-sized models based on their parameters at the trade-off of precision.

In conclusion, properly sizing VRAM is the base of any successful AI deployment. Whether inference, training, or fine-tuning workloads are running, memory is ultimately what determines what workloads the infrastructure can support. Understanding how VRAM is consumed and how important is leveraging techniques like quantization or LoRA, enterprises can make smarter hardware decisions, and avoid costly bottlenecks, and maybe turn their heads into second hand market which is still widely used.