Turing团队发布20B参数专家混合模型，每激活2B参数，性能接近9B模型，支持超长上下文。
Turing-20B-A2B是20B参数的专家混合语言模型，每激活约2B参数。该模型采用分位数路由和动态top-k配置，支持长上下文和低延迟物理AI应用。模型结合了Lightning Attention和少量全注意力层架构，预训练阶段能力超过Qwen3-8B Base并接近Qwen3.5-9B Base。原生上下文长度为128K，推理时可扩展至512K。
TuringLLM: Efficiently Scaling Foundation Models Toward Physical AI
We present Turing-20B-A2B, a 20B-parameter Mixture-of-Experts language model that activates approximately 2B parameters per token, designed for long-context and latency-sensitive physical AI applications. The model adopts Quantile Routing in a dynamic top-k configuration, enabling token-adaptive expert allocation while maintaining balanced expert utilization and a controlled average compute budget. During deployment, we further apply capacity-constrained routing to prompt prefill for more regular and efficient expert execution, while retaining dropless routing during pretraining. Turing-20B-A2B also employs a hybrid attention architecture that combines Lightning Attention with a small number of full-attention layers for efficient long-context modeling. The model is pretrained with a progressive three-stage curriculum and extended to a native context length of 128K through continued pretraining, with further inference-time extension to 512K using YaRN. Despite its compact active-parameter budget, Turing-20B-A2B achieves, at the base-model stage, overall general capability exceeding Qwen3-8B Base and approaching Qwen3.5-9B Base, while maintaining strong long-context performance and favorable prefill-latency scaling. These results demonstrate an effective balance among model capability, long-context scalability, and practical inference efficiency.