How Much VRAM Do You Need for LLMs? A Detailed Guide for Training/Fine-Tuning/Inference

Introduction

The emergence of Large Language Models (LLMs) has opened exciting possibilities for many industries and enthusiasts. However, these powerful AI systems require substantial computing resources, particularly GPU memory (VRAM). Whether you’re a software engineer, hobbyist, or data scientist looking to work with these models on your own hardware, understanding these requirements is essential.

What Are LLMs and Why Do They Need So Much Memory?

Before diving into the technical details, let’s clarify what LLMs are: they’re artificial intelligence systems trained on vast amounts of text data to understand and generate human-like language. Think of them as extremely sophisticated prediction engines that can complete sentences, answer questions, write essays, and even code.

These models consist of billions of adjustable settings (called “parameters”) that the AI uses to make its predictions. The more parameters, the more nuanced the model’s understanding can be—but also the more memory required.

Understanding VRAM: The Crucial Resource

VRAM (Video Random Access Memory) is the dedicated memory on your graphics card (GPU). While originally designed for rendering graphics in games and applications, modern GPUs have become essential for AI work due to their ability to perform many calculations simultaneously.

Think of VRAM as desk space for your AI work:

• Too little space: You’ll constantly be shuffling papers around (slow performance) or won’t be able to fit your project at all (crashes or failure to load)
• Adequate space: You can work efficiently without constant reorganizing

Unlike regular RAM, VRAM sits directly on the GPU, allowing for much faster access to the data needed for AI computations.

Training LLMs from Scratch

Training an LLM from scratch is like teaching a child to understand and speak a language from birth—it requires the most resources and time.

Key Concepts Explained:

1. Model Parameters

• What are parameters?: Parameters are like the adjustable knobs and dials that the model tweaks as it learns. Each parameter is just a number that gets updated during training.
• Scale: Modern LLMs have billions of parameters—LLaMA-3 has 8 billion, while GPT-4 is estimated to have over 1 trillion!
• Memory impact: Each parameter must be stored in memory, and the precision matters:
  • FP32 (32-bit floating-point): High precision but memory-intensive (4 bytes per parameter). This is like measuring each ingredient in a recipe down to the microgram.
  • FP16/BF16: These use half the memory (2 bytes per parameter) with acceptable accuracy for most uses. Like measuring to the nearest gram instead.

Simple math: A 7 billion parameter model in FP16 precision requires at least 14GB just to store the model (7B × 2 bytes), before considering anything else!

2. Optimizer State

• What is an optimizer?: This is the algorithm that adjusts all those parameters during training. It’s like a teacher guiding the learning process.
• Memory footprint: Optimizers need to keep track of additional information for each parameter:
  • Adam/AdamW (popular optimizers): Store 2-3 extra values per parameter, requiring 8-12 bytes per parameter
  • SGD with momentum: A simpler approach requiring 4-8 bytes per parameter

In practical terms: For a 7B parameter model using Adam, the optimizer might need an additional 56GB of VRAM (7B × 8 bytes)!

3. Activations and Gradients

• Activations: These are the intermediate results produced as data flows through the model. Think of them as the model’s “working memory” while it processes information.
• Gradients: These indicate how to adjust each parameter to improve results, like feedback signals. Gradients typically need the same amount of memory as the model parameters themselves.
• Batch size impact: This is how many examples the model processes at once. Larger batches need more memory but can speed up training.

Memory-saving techniques:

• Gradient Accumulation: Take smaller bites of data and accumulate the results before updating (saves memory but takes longer)
• Gradient Checkpointing: Only save some activations and recalculate others when needed (trades computation for memory savings)

4. Model Parallelism

• What is it?: A way to split a large model across multiple GPUs when it’s too big for one.
• Types explained:
  • Tensor Parallelism: Splitting individual operations across GPUs, like having multiple people each work on one part of a calculation
  • Pipeline Parallelism: Different GPUs handle different layers of the model, like an assembly line

The Total Picture

For training, you need to account for:

1. Model parameters (2-4 bytes per parameter)
2. Optimizer states (4-12 bytes per parameter)
3. Gradients (2-4 bytes per parameter)
4. Activations (varies based on model architecture and batch size)
5. Memory fragmentation (typically adds 10-20% overhead)

Example with Numbers:

A 7 billion-parameter model in FP16 precision with Adam optimizer, medium batch size, and efficient memory techniques might require approximately:

• Model: 14GB (7B × 2 bytes)
• Optimizer: 56GB (7B × 8 bytes)
• Gradients: 14GB (7B × 2 bytes)
• Activations: ~30GB (varies widely)
• Overhead: ~20GB (fragmentation and other memory uses)
• Total: ~137.5GB VRAM

This is why training from scratch typically requires multiple high-end GPUs or access to specialized computing clusters.

VRAM for Fine-tuning: A More Practical Approach

Fine-tuning is like teaching someone who already speaks English to understand medical terminology or legal jargon—it builds on existing knowledge, requiring significantly less resources than starting from zero.

Approaches Explained:

• Full Fine-tuning: Updates all model parameters, requiring similar memory to training (though often with smaller batch sizes)
• Parameter-Efficient Fine-tuning: Only updates a small subset of parameters, dramatically reducing memory needs

Common Methods (With Simplified Explanations):

• LoRA (Low-Rank Adaptation): Instead of adjusting the entire model, LoRA attaches small “adapter modules” to key areas. Like adding small post-it notes to specific pages in a book rather than rewriting the entire text.

  • Rank: Determines the size of these adapter modules (higher rank = more capacity but more memory)
  • Alpha: Controls how strongly the adapters influence the model’s behavior

• QLoRA: Combines quantized models (using less precise number formats) with LoRA adaptation. This is like compressing the original book to save space, then still using post-it notes for your changes.

Real-world Impact:

While full fine-tuning of a 7B parameter model might require 100GB+ VRAM, QLoRA can reduce this to as little as 8-16GB, making it accessible on consumer-grade GPUs like the NVIDIA RTX 4070 Ti SUPER.

Practical scenario: Fine-tuning Llama-2-7B with QLoRA can often be done on a single consumer GPU with 24GB VRAM (like an RTX 3090), while full fine-tuning would require multiple high-end GPUs.

VRAM for Inference: Running the Trained Model

Inference is using the trained model to perform tasks—like having a conversation with ChatGPT. This generally requires less memory than training or fine-tuning.

Important Concepts (Simplified):

1. Model Size and Precision

• The base model size still matters, but during inference, we can often use even lower precision:
  • INT8 (8-bit integers): 1 byte per parameter
  • INT4 (4-bit integers): 0.5 bytes per parameter

Example: A 7B parameter model quantized to INT4 might need just 3.5GB for the model itself!

2. KV Cache (Key-Value Cache)

• What is it?: When generating text, the model needs to remember what it’s already written. The KV cache stores this information.
• Memory impact: This grows with the length of text processed and generated.
• Context window: This determines how much previous text the model can “see” and significantly affects memory usage.

Think of it like this: A model with a 4,000 token context window needs to track 4× more information than one with a 1,000 token window.

3. Attention Mechanisms

Various attention types affect memory usage:

• Multi-head Attention (MHA): The standard approach, but more memory-intensive
• Multi-query/Grouped-query Attention (MQA/GQA): More efficient variations that share some calculations

4. Optimizations for Consumer Hardware

• Quantization: Converting the model to use smaller number formats (like INT8 or INT4)
• Flash Attention: More efficient algorithms for key calculations
• CPU Offloading: Moving parts of the model to regular RAM when they’re not actively needed

Practical VRAM Examples for Inference:

For running a 7B parameter language model (like Llama-2-7B):

• Standard settings (FP16): ~16GB VRAM
• With 4-bit quantization: ~6GB VRAM
• With 4-bit quantization + small context: ~4GB VRAM

Introducing the VRAM Calculator Tool

I wrote a simple browser app based on what we discussed above. Check out LLM VRAM Calculator, a comprehensive tool providing precise memory estimations. This user-friendly tool allows you to experiment with different settings and see their impact on memory requirements.

You can clone the GitHub repository as well:

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git clone https://github.com/SaehwanPark/llm_vram_calc.git
cd llm_vram_calc
pip install -r requirements.txt
streamlit run llm_vram_calc.py

The calculator lets you:

• Select model sizes and types
• Choose precision formats
• Adjust batch sizes and sequence lengths
• See memory requirements for training, fine-tuning, and inference

Consumer GPU Options and Their Capabilities

To help you choose the right hardware, here’s a quick reference of popular GPUs and what they can handle:

For inference, systems with unified memory architectures like Apple Silicon (e.g., Apple M2 Ultra) or AMD Ryzen AI MAX often offer superior cost-efficiency by eliminating memory transfer bottlenecks. For example, an Apple M2 Ultra MacStudio with 192GB unified memory can handle models up to 70B parameters (with FP32) in size - much larger than what’s possible on consumer GPUs with dedicated VRAM. While these platforms excel at memory allocation, other factors including compute capability and memory bandwidth significantly impact training speed and inference throughput. I’ll explore these performance considerations in detail in a future post focused on optimizing LLM workloads across different hardware platforms.

Wrapping Up

Understanding VRAM requirements is crucial for anyone working with LLMs on local hardware. By grasping these concepts and using efficient techniques, you can make smart choices about which models will work with your existing hardware or what hardware investments make sense for your goals.

Remember that the field is rapidly evolving, with new optimization techniques constantly emerging to run larger models on less powerful hardware. Our interactive calculator offers practical guidance tailored to your specific requirements, ensuring your AI journey is both accessible and resource-efficient.

Whether you’re a developer looking to integrate AI into your applications, a researcher exploring new possibilities, or an enthusiast experimenting with these powerful tools, I hope this guide helps you explore the exciting world of large language models with confidence!