Vincent Laufer, MD, PhD on LinkedIn: Model & API Providers Analysis | Artificial Analysis (2024)

Excellent data visualizations comparing all the AI model providers. Useful and comprehensive.#ai #api #llm #models #developers #providers #dataviz

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Understand the #AI landscape and choose the appropriate model and API provider for your use-case.

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This benchmarking will help you to choose the right model for your specific use case.

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This is super helpful when picking the right model

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The new benchmarking site (https://lnkd.in/g3Y-Zj3W) is a fantastic tool for developers to compare the speed of different LLM API providers. This complements existing resources like LMSYS Chatbot Arena, Hugging Face open LLM leaderboards, and Stanford's HELM, which focus on output quality. Faster token generation is crucial for agentic workflows.

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You're right, benchmarks that focus on speed alongside existing quality-focused benchmarks like LMSYS, Hugging Face, and HELM are valuable for developers. Here's why faster token generation with LLMs is crucial for agentic workflows:**Reduced Latency:*** Faster token generation means quicker responses from the LLM, leading to a more natural and engaging user experience in chatbots and virtual assistants. Users won't perceive lags or delays between their prompts and the LLM's responses.**Improved Efficiency:*** Agentic workflows often involve real-time interactions where the LLM needs to process information and respond swiftly. Faster generation allows for handling more user requests within a shorter timeframe, improving overall efficiency.**Enhanced Realism:*** In agentic systems, the LLM acts as an agent or persona. Speedy generation helps maintain the illusion of a responsive and intelligent entity. Slow response times can break this illusion and make the interaction feel clunky.**Better Scalability:*** Faster token generation enables handling a higher volume of user interactions. This becomes crucial as agentic applications scale and serve a larger user base.Overall, faster token generation with LLMs paves the way for more fluid, efficient, and realistic agentic workflows. It allows for smoother interactions, improved user experience, and better scalability for real-time applications.

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AI Benchmarking Model for developersInteresting that #Claude comes second in terms of quality output in this benchmarking model, aimed at helping developers pick which models to use - based on criteria like speed, quality of output and others.. First is #paidfor ChatGPT4, but if like me you want to experiment and test for free until you make up your mind whether it’s worth paying a subscription, then Claude is a good enough option.

A great resource for AI developers: LLM API speed benchmark. Essential for optimizing model selection. #AIBenchmark #LLMPerformance #ai2rationalize

Choosing the right #vendor or #model in an ever-expanding landscape is getting wild.A good on-top analysis could improve solution SLA compliance, time to market and reduce system over-engineering. A unified and agnostic benchmarking framework is needed to evaluate model and vendor capabilities against either functional or non-functional requirements (which are often not defined or understood). Always thanks for the useful insights Andrew Ng ! 🤖#MachineLearning #Systems #Performance #Benchmarking

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beatuful and clear report

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