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 num...
| Authors | Yuheng Zhang, Yizhao Wang, Da Zhu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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