How to estimate GPU memory for LLMs?

Calculating GPU memory for running LLMs locally

With the rise of massive LLMs like GPT, Llama, and Mistral, one of the biggest challenges for AI practitioners is figuring out how much GPU memory they need to serve these models efficiently. GPU resources are expensive and scarce, so optimizing memory allocation is crucial.

This guide will walk you through a simple yet effective formula to estimate the GPU memory required to serve an LLM. Whether you’re deploying models for inference or fine-tuning them for specialized tasks, this knowledge will help you plan your infrastructure effectively.

Data Science in Your Pocket – No Rocket Science

The Formula for GPU Memory Estimation

To calculate the GPU memory needed for serving an LLM, we use the following equation:

Understanding the Parameters:

M: Required GPU memory in Gigabytes (GB)

P: Number of parameters in the model (e.g., a 7B model has 7 billion parameters)

4B: 4 bytes per parameter (assuming full precision FP32)

32: There are 32 bits in 4 bytes

Q: Bits used per parameter for model storage (e.g., FP16 = 16 bits, INT8 = 8 bits, etc.)

1.2: Represents a 20% overhead for additional memory needs such as activation storage, attention key-value caches, etc.

This formula helps you determine how much GPU memory is required to load a model into memory while accounting for different quantization levels and overhead.

Step-by-Step Breakdown

Let’s say you want to estimate the GPU memory required for Llama 70B in FP16 precision.

Given:

• P = 70B (70 billion parameters)
• Q = 16 (since we are using FP16 precision)
• Overhead factor = 1.2

Now, applying the formula:

Converting to GB:

Since 1 GB = 1⁰⁹ bytes, we divide by 1⁰⁹:

So, to load Llama 70B in FP16, you would need 168GB of GPU memory.

What Happens with Quantization?

Quantization allows us to store model weights in lower precision, reducing memory requirements. Here’s how much memory Llama 70B would need in different bit formats:

Precision (Q) GPU Memory Required

FP32 (32-bit) 336 GB

FP16 (16-bit) 168 GB

INT8 (8-bit) 84 GB

4-bit Quantization 42 GB

Key Takeaways:

• Lower precision models require significantly less GPU memory.
• 4-bit quantization is extremely memory-efficient, allowing massive models to fit within consumer GPUs like the RTX 4090 (24GB VRAM).
• FP16 is the industry standard for balancing performance and memory usage.

Optimizing Your Model Deployment

If your GPU memory is limited, here are some optimization strategies:

1. Use Quantization: Convert your model to 8-bit or 4-bit to reduce the memory footprint.
2. Offload to CPU: Some weights can be offloaded to the CPU, reducing GPU memory usage.
3. Use Model Parallelism: Split model weights across multiple GPUs.
4. Optimize KV Cache: Reduce the number of stored attention key-value pairs.
5. Leverage Efficient Serving Frameworks: Use tools like vLLM or TensorRT-LLM for optimized inference.

Conclusion

Calculating GPU memory for serving LLMs is essential for scaling AI applications efficiently. Using the simple formula above, you can estimate the VRAM required for different precision levels and optimize your deployment accordingly.

If you’re working with massive models like Llama 70B, quantization and parallelism are your best friends to keep GPU costs manageable. By applying these optimizations, you can serve powerful AI models without breaking the bank on high-end hardware.

Hope this helps

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