LLM RAM Calculator

The LLM RAM Calculator provides estimates based on typical memory requirements for models of different sizes and quantization levels. It offers a good general guideline, but actual memory usage may vary depending on implementation details, context length settings, and the specific framework used to run the model.

While the RAM Calculator is primarily designed for estimating hardware requirements for local deployment, the memory estimates can help inform cloud deployment decisions. Many cloud providers charge based on the instance size required, which directly relates to RAM requirements. However, for precise cloud costs, you should refer to the specific pricing of your chosen provider.

Quantization is a technique that reduces model precision by representing weights with fewer bits (e.g., 8-bit or 4-bit instead of 32-bit or 16-bit). This significantly reduces memory requirements, often with minimal impact on performance. For example, 8-bit quantization typically cuts memory usage by 50-75% compared to full precision, making larger models viable on consumer hardware.

Running a 70B parameter model on a 16GB laptop is challenging even with extreme quantization. Such models typically require 35-140GB of RAM depending on quantization level. However, you might be able to use specialized solutions like llama.cpp with 4-bit quantization and techniques like offloading to disk, though performance would be severely impacted. For better results, consider using smaller models (7B-13B) on limited hardware.

Context window size directly impacts memory usage beyond the base model requirements. As a rough estimate, each 1K tokens of context requires approximately 1-2MB of extra memory in 16-bit precision. For very long contexts (e.g., 32K tokens), this can add several hundred megabytes to the memory requirements. Our calculator focuses on base model size, so factor in additional memory for long contexts.