how many gpus needs when full training 70B Llama2

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how many gpus needs when full training 70B Llama2

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Training or running inference on large language models like LLaMA 2 70B requires substantial computational resources, particularly in terms of GPU memory (VRAM). The LLaMA 2 70B model, with its 70 billion parameters, presents specific challenges due to its size. Here's a detailed breakdown of the requirements and considerations for running this model:

GPU Memory Requirements

The LLaMA 2 70B model, when fully loaded into memory, requires a significant amount of VRAM. A single fp16 parameter (the common data type used for such models) requires 2 bytes of memory. Therefore, loading the entire LLaMA 2 70B model would require approximately 140 GB of memory (70 billion parameters * 2 bytes)[2]. This far exceeds the capacity of any single consumer-grade GPU.

Quantization and Mixed-Precision Techniques

To fit the model into consumer-grade GPUs, quantization techniques can be applied to reduce the precision of the model's parameters, thus reducing the overall memory footprint. For instance, quantizing the model to 4-bit precision reduces the memory requirement to about 35 GB (70 billion * 0.5 bytes)[2], which is closer to, but still above, the capacity of high-end consumer GPUs like the NVIDIA RTX 3090 or 4090, each with 24 GB of VRAM.

Further reduction to 3-bit precision brings the requirement down to approximately 26.25 GB[2], which still doesn't fit into a single GPU but is more manageable across multiple GPUs or with certain memory offloading techniques.

Practical GPU Configurations

For practical purposes, running the LLaMA 2 70B model efficiently would require:

• Multiple GPUs: Even with quantization, a single consumer-grade GPU does not have enough VRAM to fit the entire model. The use of multiple GPUs, with technologies like NVIDIA's NVLink to facilitate fast data transfer between them, can help but also introduces complexity in terms of model parallelism and data management[1].
• High-End GPUs: GPUs with larger memory capacities, such as the NVIDIA A100 with 40 or 80 GB of VRAM, can accommodate larger portions of the model but are significantly more expensive and targeted towards enterprise or research institutions rather than individual consumers[3][4].
• Hybrid CPU-GPU Configurations: Parts of the model can be offloaded to system RAM, leveraging the CPU for certain computations. This approach allows for the use of models that exceed GPU memory limits at the cost of increased inference time due to slower memory access speeds[3].

Conclusion

Running the full LLaMA 2 70B model requires careful consideration of hardware capabilities and potentially significant investment in high-end GPUs or multiple GPU setups. Quantization and mixed-precision techniques are essential for reducing the model's memory footprint, but they also require expertise to implement effectively without significantly compromising model performance[2]. For those without access to such hardware, cloud-based solutions or model distillation techniques might offer more feasible alternatives.

Citations: [1] https://news.ycombinator.com/item?id=37067933 [2] https://towardsdatascience.com/run-llama-2-70b-on-your-gpu-with-exllamav2-588141a88598 [3] https://www.reddit.com/r/LocalLLaMA/comments/184qfeg/chassis_only_has_space_for_1_gpu_llama_2_70b/ [4] https://huggingface.co/blog/ram-efficient-pytorch-fsdp [5] https://www.hardware-corner.net/guides/computer-to-run-llama-ai-model/ [6] https://github.com/Lightning-AI/lit-gpt/issues/456 [7] https://www.interconnects.ai/p/llama-2-part-2 [8] https://hpc-ai.com/blog/70b-llama2-training