If vLLM already solved LLM serving, why did SGLang appear?
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After the launch of ChatGPT and open-source models in 2022–2023, lots of companies tried hosting models on GPU infrastructure, but they encountered issues like GPU idle time and computational bottlenecks. Then vLLM was launched by the Sky Computing Lab at UC Berkeley, which changed the game forever. Model serving became dramatically easier, and GPU utilization skyrocketed. But, if vLLM solved all the problems, why was SGLang created by fellow graduates of UC Berkeley, Stanford, and Carnegie…
1Key Takeaways
- After the launch of ChatGPT and open-source models in 2022–2023, lots of companies tried hosting models on GPU infrastructure, but they encountered issues like GPU idle time and computational bottlenecks.
- Then vLLM was launched by the Sky Computing Lab at UC Berkeley, which changed the game forever.
- Model serving became dramatically easier, and GPU utilization skyrocketed.
- But, if vLLM solved all the problems, why was SGLang created by fellow graduates of UC Berkeley, Stanford, and Carnegie….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that after the launch of ChatGPT and open-source models in 2022–2023, lots of companies tried hosting models on GPU infrastructure, but they encountered issues like GPU idle time and computational bottlenecks.
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