LLaMA Language Model Performance Optimized with GPUs
VRAM Requirements for Optimal Performance
LLaMA-65B and 70B language models demonstrate optimal performance when paired with a graphics processing unit (GPU) equipped with a minimum of 40GB video random access memory (VRAM). For contexts exceeding 32k, a GPU with over 48GB of VRAM is required, as 16k is the maximum context size that can fit within two 4090 GPUs with 24GB VRAM each.
Resource Requirements for Larger Models
Larger LLaMA models demand substantial hardware resources. For instance, a 4-bit 7B billion-parameter Llama-2 model occupies approximately 40GB of RAM. Utilizing a processor such as the Ryzen 5 5600X requires a minimum of 128-129GB RAMVRAM in FPBF16 format to load the model itself, not including memory for context, operating system, and other processes.
Memory Considerations for the Largest LLaMA Model
The most comprehensive and advanced model within the LLaMA 2 family boasts an impressive 70 billion parameters. Each half-precision (FP16) parameter weighs 2 bytes, resulting in a substantial memory footprint. Therefore, leveraging this model necessitate significant computational resources.
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