Simple Guide to Calculating VRAM Requirements for Local LLMs

Running large language models (LLMs) locally has become increasingly popular, thanks to advances in GPU hardware, accessible frameworks, and the availability of pre-trained and fine-tuned models. But one of the most frequent questions I see is: How much VRAM do I need to run this model?

When I included the post about DeepSeek's system requirements on ApX Machine Learning, I didn't anticipate the response it would receive. It quickly became a reference for many trying to understand the resources needed to handle large models. In this post, I'll explain a simple, reproducible way to approximate VRAM requirements for local LLMs. By the end, you'll have the tools to plan your hardware needs confidently.

The Formula for Calculating VRAM Requirements

At its core, determining VRAM requirements boils down to this formula:

VRAM Required = Number of Parameters (in billions) × Number of Bytes per Parameter × Overhead

This formula reliably estimates the memory needed to load and run a model. Let's explore each component in detail to understand how it affects the total.

Breaking Down the Components

1. Number of Parameters

The model's size is measured by the number of parameters it has. These parameters are the weights learned during training, which define how the model interprets input data and generates output.

Most models list their parameter counts in their documentation or on platforms like Hugging Face. For example:

• DeepSeek-7B: 7 billion parameters.
• LLaMA-13B: 13 billion parameters.
• Stable Diffusion U-Net: 860 million parameters

This number is often the first clue about how much memory you'll need when choosing a model to run locally.

2. Bytes Per Parameter

The memory needed per parameter depends on the precision of the model. Precision refers to how numbers are represented during computation, directly affecting VRAM usage. The most common types include:

• FP16 (16-bit floating point): This is the default for many LLMs and requires 2 bytes per parameter.
• FP32 (32-bit floating point): Rarely used for inference due to its higher memory requirements (4 bytes per parameter). Typically, it is only used for training.
• Quantized Models: Optimized for lower precision, saving significant memory. Common types are:
  • 8-bit Quantization: 1 byte per parameter.
  • 4-bit Quantization: 0.5 bytes per parameter.

Quantized models are particularly useful for running larger models on consumer-grade GPUs, where VRAM is limited.

3. Overhead

The overhead accounts for additional VRAM usage beyond the parameters themselves. This includes:

• Activations: Temporary data generated during forward passes (inference) or backward passes (training).
• Optimizer States: If you're fine-tuning or training a model, optimizer states like gradients can double or triple the memory footprint.
• Framework-Dependent Factors: GPU frameworks like PyTorch or TensorFlow often consume extra memory for operations like tensor allocation or caching.

While the exact overhead varies, a 15% buffer (or multiplying by 1.15) is a reasonable middle point for most scenarios. This figure works well for many FP16 models during inference. However, the overhead increases significantly for fine-tuning or larger batch sizes.

Overhead Table by Model Type

These ranges give a better idea of how different tasks and models may require varying amounts of extra VRAM for operations beyond just parameters.

Applying the Formula

Let's say you plan to run LLaMA-13B locally on your system. The model has:

• 13 billion parameters.
• Default precision of FP16 (2 bytes per parameter).
• Estimated overhead of 15% (1.15).

Using the formula: VRAM Required = 13 × 2 × 1.15 = 29.9 GB.

This means you'd need a GPU with at least 30 GB of VRAM to run the model comfortably in its base FP16 format. If you instead use a 4-bit quantized version, the requirement drops significantly: 13 × 0.5 × 1.15 = 7.5 GB.

With quantization, you could run LLaMA-13B on a GPU with around 8 GB of VRAM, something achievable on many consumer-grade GPUs.

Considerations for Fine-Tuning and Larger Workloads

Running a model for inference is one thing, but fine-tuning or training introduces additional complexities. These processes require extra VRAM for storing gradients, optimizer states, and larger activations. Here's what to keep in mind:

1. Batch Size: Increasing batch size raises memory requirements due to larger activation storage. Reducing batch size is often necessary for training larger models on limited hardware.
2. Gradient Accumulation: When VRAM is limited, gradient accumulation can simulate larger batch sizes by splitting them into smaller chunks, but this comes at the cost of slower training.
3. Mixed Precision Training: This technique combines FP16 and FP32 computations to reduce memory usage while maintaining numerical stability. Frameworks like NVIDIA's Apex make this straightforward to implement.

For training workloads, expect the VRAM requirements to be 2–3× higher than for inference.

Benchmarking for Accuracy

While the formula provides a reliable estimate, real-world VRAM usage can vary based on factors like the model's architecture and your specific hardware setup. To get precise numbers, you can benchmark the model on your GPU:

1. Load the Model: Use a framework like PyTorch or TensorFlow to load the model into memory.
2. Monitor GPU Usage: Tools like nvidia-smi or PyTorch's torch.cuda.memory_allocated() let you track real-time memory consumption.
3. Iterate with Configuration: Experiment with precision, batch size, and quantization to optimize performance within your hardware limits.

In my own tests, the formula's estimates align within 5% of the measured VRAM usage for most scenarios. This consistency makes it a dependable tool for planning your hardware needs.

Optimizing for Limited Hardware

If your GPU doesn't meet the VRAM requirements for a given model, there are still options:

• Quantization: Reducing precision (e.g., from FP16 to 4-bit) can significantly cut memory usage without drastically affecting performance.
• Model Sharding: Split the model across multiple GPUs to distribute the VRAM load. Frameworks like Hugging Face Accelerate make this easier to implement.
• Offloading: Offload parts of the model to CPU or even disk, though this may reduce inference speed.
• Low-Rank Adaptation (LoRA): For fine-tuning, LoRA reduces memory usage by modifying only a subset of the model's parameters.

These techniques make it possible to run larger models on modest hardware setups, though there may be trade-offs in speed or complexity.

Conclusion

Estimating VRAM requirements for running local LLMs is simpler than it seems when you break it down into clear components: model parameters, precision, and overhead. By using the formula VRAM = Parameters × Bytes × Overhead, you can make informed decisions about which models your hardware can handle and where optimizations might be needed.

While the formula gives a reliable estimate, benchmarking remains the best way to confirm your system's capability, especially for fine-tuning or larger workloads. With quantization and other optimization techniques, even consumer-grade GPUs can now handle impressive models locally.