Kimi K2 Thinking: 1T-A32B params, SOTA HLE, BrowseComp, TauBench && Soumith leaves Pytorch
Moonshot AI launches Kimi K2 Thinking, a 1T-parameter MoE model with 32B active experts, 256K context, and top benchmark scores on HLE, BrowseComp, and agentic tool-use tasks.
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.