技术规格

系统要求

不同量化方法和上下文大小的显存要求

Hunyuan Turbo

Tencent Hunyuan Turbo S is a high-performance language model engineered for rapid text generation and efficient analytical reasoning. This model aims to deliver near-instantaneous responses, significantly reducing initial word latency and enhancing overall output speed. It is designed to function as a foundational element for the development of advanced applications requiring sophisticated reasoning, extensive text processing capabilities, and robust code generation.

The architectural design of Hunyuan Turbo S incorporates a hybrid Mamba-Transformer fusion within a Mixture of Experts (MoE) framework. This represents an integration of the Mamba state-space model into a super-large MoE, balancing Mamba's efficiency in processing long sequences with the Transformer's proficiency in complex contextual understanding. A key innovation in its architecture is the implementation of a "fast thinking" and "slow thinking" paradigm. "Fast thinking" enables quick, intuitive responses for routine queries through optimized word speed and reduced latency. "Slow thinking," which draws knowledge from the Hunyuan T1 model, facilitates deliberate analytical processing essential for intricate problem-solving in domains such as mathematics, logical deduction, and scientific inquiry. The model further optimizes computational efficiency and reduces KV-Cache usage by employing Grouped Query Attention (GQA) and Cross-Layer Attention (CLA) strategies.

Hunyuan Turbo S is optimized for strong performance across a range of linguistic and analytical tasks, including knowledge acquisition, mathematical computations, and creative content generation. Its design emphasizes efficiency, leading to reduced computational complexity and lower inference costs. These performance characteristics render the model suitable for deployment in applications demanding swift and accurate text outputs, such as intelligent customer support systems, interactive chatbot interfaces, and various enterprise AI solutions where both response time and economic efficiency are critical operational considerations. The model is also capable of handling extended context lengths, which supports maintaining coherence and relevance in prolonged conversational or document-based interactions.

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