Performance optimization techniques
After distributed tranining, LLM practitioners use performance & memory optimization techniques.There are 3 techniques for this.
1.Mixed-Precision training
This method uses lower-precision arithmetic and reduces resource utilization. It reduces the workload on CPU and lowers the use of storage. Because of this, we can deploy larger networks with same amount of memory.
2.Gradient Checkpoint
This technique stores only subset of intermediate activations and recomputing them during backward pass to reduce memory usage.
3.Operator Fusion
Using this technique, we can combine multiple operations into a single one to reduce memory allocation.
Using Purpose-Built Infrastructure
1.AWS Trainium
It is second-generation machine-learning accelerator built for deep-learning training.It powers EC2-Trn1 instances.
2.AWS Inferentia
It delivers high performance at lowest cost for deep-learning applications. Inf2 instances are used for large-scale gen-AI applications. They use models containing billions of parameters.
LLM practioners can use AWS neuron SDK for HPC.
Thank You
Top comments (1)
Hi I found a opensource, hope it can help.
Enova focuses on LLM Serving scenarios, assisting LLM developers in deploying their trained, fine-tuned, or industry-standard open-source large language models with a single click. It provides adaptive resource recommendations, facilitates testing through the injection of common LLM datasets and custom methods, offers real-time monitoring of service status with visualization of over 30 request metrics, and enables automatic scaling, all aimed at significantly reducing the costs of model deployment and improving GPU utilization for LLM developers
github.com/Emerging-AI/ENOVA