LLM-FinOps: The Key to Cost-Effective Gen AI Applications
Discover LLM-FinOps: The art of balancing cost, performance, and scalability in AI, where strategic cost monitoring meets innovative perform| TensorOps
Discover LLM-FinOps: The art of balancing cost, performance, and scalability in AI, where strategic cost monitoring meets innovative perform| TensorOps
Explaining Mixture of Experts LLM (MoE): GPT4 is just 8 smaller Expert models; Mixtral is just 8 Mistral models. See the advantages and disadvantages of MoE. Find out how to calculate their number of parameters.| TensorOps
Co-written with Gad BenramThe sophistication of large language models, like Google's PaLM-2, has redefined the landscape of natural language processing (NLP). These models' ability to generate human-like text has opened up a vast array of applications, including virtual assistants, content generation, and more. To truly leverage these models' potential, an efficient approach is needed: Prompt Engineering. This blog post aims to elucidate key design patterns in prompt engineering, complete with r| TensorOps
Quantization is a technique used to compact LLMs. What methods exist and how to quickly start using them?| TensorOps
