How much GPU memory do you need to deploy a large language model like Llama 70B for real-world applications? In this video, I’ll break down a simple method to estimate GPU memory requirements based on your use case.
Key Points Covered:
- Understanding GPU Memory Consumption
Learn about the two major contributors to memory usage during inference:- Model Weights: For the Llama 70B model, these take up 140 GB of GPU memory.
- KV Cache: A crucial caching mechanism for efficient token generation, which scales with the number of users, context size, and tokens.
- Memory Calculation Example
Using a maximum context size of 32K tokens and 10 concurrent users:- KV Cache requires 800 GB of memory.
- Activations, buffers, and overhead add an estimated 94 GB.
This brings the total GPU memory requirement to approximately 1034 GB for optimal performance.
- Single-User vs. Multi-User Scenarios
- For a single user, memory requirements drop significantly to around 220 GB.
- Context size and optimization can further influence memory consumption.
- Optimization Techniques
Stay tuned for future videos where we’ll discuss how to optimize memory usage during inference to reduce costs and improve efficiency.
For detailed information, please watch our YouTube video: How Much GPU Memory is Needed for LLM Inference?
