Moonshot AI 发布 Kimi K3

Beijing-based Moonshot AI's Kimi K3 just dropped. - 1 Mn token context window, natively multimodal. (i.e. 750,000 words of code or documentation in a single prompt.) - Total 2.8 trillion params, but activates only 16 of its 896 experts at a time. That is roughly 1.8% of the expert pool per token. - Some of the benchmarks put it Opus 4.8/ GPT 5.6 Sol / Fable 5 territory. - Its Delta Attention enables up to 6.3x faster decoding in million-token contexts - available via API at $3/$15 per million input/output tokens - K3’s 2.8T MXFP4 weights require roughly 1.4 TB before quantization metadata and runtime overhead. At an assumed 115 GB usable per GB10 node (NVIDIA’s compact Grace Blackwell AI chip), 14–16 nodes is expected to hold the raw weights realistically. Including activations, KV cache and runtime memory etc. Moonshot itself recommends supernodes containing 64 or more accelerators. Some very cool findings from technical report - K3 autonomously designed, optimized, and verified a working AI chip in a single 48-hour run—specifically to serve a smaller model built on K3’s own architecture. The simulated chip reportedly reached 8,700+ tokens/second, contained 1.46 million standard cells, and fit within 4 mm². - An early version of K3 handled the majority of the kernel-optimization work used to develop K3 itself. - K3 built a GPU compiler from scratch. created MiniTriton, optimization passes, PTX generation, and runtime. Matched or beat Triton on some workloads and successfully trained nanoGPT end to end. - In a 15-hour autonomous run, Kimi K3 redesigned a production-scale training kernel and cut forward-plus-backward time from 283.6 ms to 114.4 ms. - For a 42-year semiconductor-industry report, Kimi says K3 performed 2,800+ web searches/fetches, 1,100+ terminal data pulls, processed 11,000+ pages, and recursively improved the work over 120+ rounds. - K3 reproduced a computational-astrophysics research workflow in roughly two hours, versus an estimated one to two weeks for an experienced researcher. It reviewed 20+ papers, evaluated 300+ equations of state, found inconsistencies in published formulas, and wrote 3,000+ lines of Python.  > **引用原帖 Kimi.ai (@Kimi_Moonshot):** > Introducing Kimi K3: Open Frontier Intelligence > 🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal > 🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts > 🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost > 🔹 Built for long-horizon agentic coding and self-evolving workflows > Kimi K3 is now live on on https://t.co/zrk6zZxZUo, Kimi Work, Kimi Code, and the Kimi API. > Open Weights by July 27, 2026. > 🔗 API: https://t.co/XCrgjXAqMw > 🔗 Tech blog: https://t.co/YTfiMSNM1f > https://x.com/Kimi_Moonshot/status/2077830229968683203 Rohan Paul (@rohanpaul_ai): Kimi K3 appears to beat every model shown on BrowseComp while costing only a fraction as much per task. Its claiming a dramatically better intelligence-per-dollar tradeoff. BrowseComp assesses the capability of an AI agent to repeatedly search the internet and discover obscure, hard-to-find facts that require multiple searches and validation. i.e. a high BrowseComp score means you are good at deep research and web investigation, but not necessarily better general intelligence, writing, coding, or everyday browsing.