StepFun has released the BF16 and FP8 weights of Step 5 Preview on Hugging Face and ModelScope, according to RadixArk's post.
The model is a sparse MoE with 600B total and 27B active parameters per token, a 1M-token context window, and native text and vision input.
RadixArk says it helps power StepFun's first-party production API with SGLang, and a deployment guide is available in the SGLang docs.
The post identifies the open-weight release channels and the serving stack, showing how a 600B-parameter MoE model is deployed through SGLang in production.
Congratulations to @StepFun_ai on Step 5 Preview going open weights! RadixArk is proud to help power StepFun's first-party production API with SGLang at scale.
Production inference means great token economics under strict latency requirements. SGLang and its cache-aware routing stack now bring that to Step 5 Preview in proven production traffic.
Building with Step 5 Preview? Deploy with SGLang today! https://docs.sglang.io/cookbook/autoregressive/StepFun/Step-5-Preview
Step 5 Preview is now open weights. BF16 and FP8 weights are live on Hugging Face and ModelScope, together with our technical blog. • Sparse MoE: 600B total, 27B active per token • 1M-token context window • Native text and vision input • Built for long-horizon agentic work: coding, software engineering, professional knowledge work and finance Frontier intelligence, open weights. Build what's next. Hugging Face: https://huggingface.co/stepfun-ai/Step-5-Preview ModelScope: https://modelscope.ai/models/stepfun-ai/Step-5-Preview GitHub: https://github.com/stepfun-ai/Step-5-Preview
Source: RadixArk · x.comPublished
