Gradient makes LLM benchmarking cost-effective and effortless with AWS Inferentia

This article discusses how Gradient, a company that develops custom large language models (LLMs), uses AWS Inferentia to cost-effectively benchmark and evaluate the performance of their LLMs during pre-training and fine-tuning stages.

Specifically, the article covers:

• Challenges faced by Gradient in benchmarking LLMs using the open-source lm-evaluation-harness tool, such as limitations in VRAM and GPU instance availability
• Integration of AWS Neuron and AWS Inferentia into lm-evaluation-harness, enabling access to larger shared accelerator memory and cost savings through AWS Spot Instances
• Results showing comparable performance between AWS Inferentia2 and original systems for benchmarking tasks like gsm8k, with significant time savings
• Step-by-step instructions for deploying and running lm-evaluation-harness on AWS Inferentia2 instances with models like Gradient's v-alpha-tross and Mistral-7B
• Conclusion highlighting the benefits of using AWS Inferentia for cost-effective and efficient LLM benchmarking during custom LLM development