How Much VRAM Do You Really Need to Run a 70B LLM?
How Much VRAM Do You Really Need for a 70B LLM? Running a large language model locally sounds simple until you start looking at GPU memory. A model has 70 billion parameters. Your GPU has 24 GB, 32 GB, 48 GB, or maybe 80 GB of VRAM. So will it fit? Unfortunately, parameter count alone does not answer that question . To estimate how much VRAM an LLM actually needs, you need to consider at least…
Determining the appropriate amount of video memory (VRAM) needed to run a 70 billion parameter language model (LLM) locally is more complex than simply matching the parameter count to the GPU's memory capacity. While the sheer number of parameters is a starting point, several additional factors must be considered:
1. Model weights: The initial calculation estimates the RAM needed for model weights, which vary based on the precision used (e.g., FP16, INT8, 4-bit quantization). For a 70 billion-parameter model, raw weights require around 140 GB at FP16 precision, 70 GB at INT8, and between 35 GB and 43.75 GB at different quantization levels.
2. Quantization formats: The choice of quantization impacts memory requirements due to factors like scales, metadata, higher-precision tensors, and other overheads. A model quantized to 4 bits may not directly equate to 35 GB of VRAM, as the actual memory usage could be slightly different.
3. KV cache: The Key-Value (KV) cache stores information from previous tokens, growing larger as conversation length increases. This cache can significantly impact memory usage, especially with longer contexts.
4. Runtime overhead: Additional GPU memory is consumed by runtime components like temporary tensors, CUDA kernels, attention workspaces, and graph allocations.
When evaluating hardware for local LLM inference, it becomes essential to balance model size and performance requirements. While a 24 GB GPU may seem insufficient for a 4-bit 70B model (which requires around 35 GB), users may still consider this option if they are willing to make trade-offs in model fidelity and speed.
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