content.raw: Visualization of GPT-4o breaking away from the quality & speed trade-off curve the LLMs have followed thus far ✂️
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content.value: Visualization of GPT-4o breaking away from the quality & speed trade-off curve the LLMs have followed thus far ✂️
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content.raw: Key GPT-4o takeaways
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content.value: Key GPT-4o takeaways
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content.raw: ‣ GPT-4o not only offers the highest quality, it also sits amongst the fastest LLMs
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content.value: ‣ GPT-4o not only offers the highest quality, it also sits amongst the fastest LLMs
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content.raw: ‣ For those with speed/latency-sensitive use cases, where previously Claude 3 Haiku or Mixtral 8x7b were leaders, GPT-4o is now a compelling option (though significantly more expensive)  
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content.value: ‣ For those with speed/latency-sensitive use cases, where previously Claude 3 Haiku or Mixtral 8x7b were leaders, GPT-4o is now a compelling option (though significantly more expensive)  
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content.raw: ‣ Previously Groq was the only provider to break from the curve using its own LPU chips. OpenAI has done it on Nvidia hardware (one can imagine the potential for GPT-4o on Groq)
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content.value: ‣ Previously Groq was the only provider to break from the curve using its own LPU chips. OpenAI has done it on Nvidia hardware (one can imagine the potential for GPT-4o on Groq)
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content.raw: 👉 How did they do it? Will follow up with more analysis on this but potential approaches include a very large but sparse MoE model (similar to Snowflake's Arctic) and improvements in data quality (likely to have driven much of Llama 3's impressive quality relative to parameter count)
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content.value: 👉 How did they do it? Will follow up with more analysis on this but potential approaches include a very large but sparse MoE model (similar to Snowflake's Arctic) and improvements in data quality (likely to have driven much of Llama 3's impressive quality relative to parameter count)
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content.raw: Notes: Throughput represents the median across providers over the last 14 days of measurements (8x per day)
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content.value: Notes: Throughput represents the median across providers over the last 14 days of measurements (8x per day)
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content.raw: Data is present on our HF leaderboard: 
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content.value: Data is present on our HF leaderboard: 
content.raw: https://huggingface.co/spaces/ArtificialAnalysis/LLM-Performance-Leaderboard
content.id: ArtificialAnalysis/LLM-Performance-Leaderboard
content.type: space
content.type: resource
content.url: https://huggingface.co/spaces/ArtificialAnalysis/LLM-Performance-Leaderboard
content.raw:  and graphs present on our website
content.type: text
content.value:  and graphs present on our website
rawContent: Visualization of GPT-4o breaking away from the quality & speed trade-off curve the LLMs have followed thus far ✂️
  
  Key GPT-4o takeaways
  ‣ GPT-4o not only offers the highest quality, it also sits amongst the fastest LLMs
  ‣ For those with speed/latency-sensitive use cases, where previously Claude 3 Haiku or Mixtral 8x7b were leaders, GPT-4o is now a compelling option (though significantly more expensive)  
  ‣ Previously Groq was the only provider to break from the curve using its own LPU chips. OpenAI has done it on Nvidia hardware (one can imagine the potential for GPT-4o on Groq)
  
  👉 How did they do it? Will follow up with more analysis on this but potential approaches include a very large but sparse MoE model (similar to Snowflake's Arctic) and improvements in data quality (likely to have driven much of Llama 3's impressive quality relative to parameter count)
  
  Notes: Throughput represents the median across providers over the last 14 days of measurements (8x per day)
  
  Data is present on our HF leaderboard: https://huggingface.co/spaces/ArtificialAnalysis/LLM-Performance-Leaderboard and graphs present on our website
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