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Exciting groundbreaking research on efficient Large Language Model (LLM) inference! KVSharer, a revolutionary plug-and-play method, challenges conventional wisdom in KV cache optimization. Here’s how KVSharer works to optimize LLM inference: >> Strategy Search Process Step 1:… https://t.co/L11qT04eGQ
Looking to scale LLM Inference and save on costs? @basetenco’s benchmark post breaks down batch handling, goes deep into performance results, and provides tips on when and how to optimize spend. Get the full scoop here: https://t.co/5PMQcmQB3D
New blog: Optimize your AI application with semantic cache ⏲️ Learn: - Caching LLM responses to speed up your AI application - How caching reduces LLM costs - Difference between semantic cache and key-value cache https://t.co/4FeTWpWXoa
Recent developments in the field of large language models (LLMs) highlight significant advancements in cost efficiency and performance. A cost-optimization sprint revealed that GPT-4o operates at a cost of $4.38 per million tokens, while open-source models can be utilized for as low as $1.50 per million tokens. Additionally, fine-tuning open-source models, such as Meta's Llama 3.1, has shown to outperform commercial models like GPT-4 by nearly 20% across 30 benchmark tasks. This trend underscores the growing competitiveness of open-source LLMs against established commercial counterparts. Furthermore, new strategies for optimizing LLM inference, including the KVSharer method, are being researched to enhance efficiency in cache optimization.