Kimi K2 Thinking: 1T-A32B params, SOTA HLE, BrowseComp, TauBench && Soumith leaves Pytorch

**Moonshot AI** launched **Kimi K2 Thinking**, a **1 trillion parameter** mixture-of-experts (MoE) model with **32 billion active experts**, a **256K context window**, and native **INT4 quantization-aware training**. It achieves state-of-the-art results on benchmarks like **HLE (44.9%)**, **BrowseComp (60.2%)**, and agentic tool use with **200-300 sequential tool calls**. The model is deployed with **vLLM** support and OpenAI-compatible APIs, available on platforms like Arena, Baseten, and Yupp. Early user reports note some API instability under launch load. Meanwhile, **Google** announced the **TPU v7 (Ironwood)** with a **10× peak performance improvement** over TPU v5p, aimed at training and agentic inference for models like **Gemini**. **Apple** added support for M5 Neural Accelerators in llama.cpp for inference acceleration.