Yining Zhang of Baseten discusses the history and development milestones of the open-source LLM inference framework SGLang.
Assertion Not checkable as stated
Zhang: Meta Failed at Training MoE Models for Llama Series
“The reason why Lama open-sourced the MOE model, because I think they tried to train our MOE model, but they failed. So that, that's why they didn't open source MOE mode for Lama series.”
Assertion Supported
Zhang: XGrammar outperforms Outlines and is integrated into TensorRT-LLM
“And I think Xgrammar's performance is better than the outline's, and also in the TensorFlow RTLM, the latest release, TensorFlow RTLM also integrates Xgrammar as the backend for the constructed coding.”
Assertion Contradicted
Yining Zhang: DeepSeek V3 scores 94.6 on GSM8K, outperforming Llama 405B
“Yeah, I think even they use the FP-A to quantization, the benchmark result is very good, such as something like GSM-HK. The score is nearly 94.6. It's so high, you know. I think it's higher than every other open source AIM, even the LAMA 400 zero five billion.”
Assertion Not checkable as stated
Zhang: Baidu and ByteDance Internal Models Use DeepSeek-Like MoE Architectures
“As far as I know, some companies such as Baidu or Baidu Dance, they are internal, the dominant AOM, they use the MOE architecture, and their ways, I think, is similar to the DeepSeq MOE model.”
Opinion
Zhang: SGLang outperforms vLLM and has better usability than TensorRT-LLM
“I think for the common use case, maybe not, not the DeepSeq VIII, for the common use case, I think SGLAN's performance is better than FLM, and its usability is better than TensorFlow TLM.”
Assertion Supported
Zhang: SGLang achieved 3x throughput over vLLM in mid-2024 benchmarks
“At that time, I think its performance is maybe three times, is throughout, put it, three times than FLM.”