@interjc: The market still needs a disruptive force; whether you use Kimi or not, the major companies' reset cycles are increasing.
Summary
Kimi releases the K3 model, featuring 2.8 trillion parameters, a million-token context window, and native multimodal capabilities. It leverages Kimi Delta Attention and Attention Residuals to enhance inference speed and training efficiency.
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Cached at: 07/17/26, 12:19 AM
The market still needs a catfish. Whether you use Kimi or not, the major companies have indeed increased their reset counts.
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
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Kimi.ai released Kimi K3, a 2.8 trillion parameter multimodal model with 1 million context, featuring novel Delta Attention and Attention Residuals, and a self-optimizing stack including MiniTriton compiler. The model achieves up to 6.3x faster decoding and ~25% higher training efficiency.
@YRSM_Simon: This is big news! Kimi 2.6 is a generative-level model. In this age of overflowing LLM capabilities, speed will become the deciding factor in competition. Is the chip sector about to see another 'sector rotation'? 😅
Cerebras is now running Kimi K2.6, a trillion-parameter model, in enterprise trials at ~1,000 tokens/s, the fastest frontier model performance ever measured by Artificial Analysis.
@0xLogicrw: According to a Nomura Securities report, Moonshot AI is developing a large model with 5 to 6 trillion parameters. Compared to Kimi K3, released in July this year, the new model's parameter count is nearly double. The biggest bottleneck right now is a shortage of high-end training compute, and the specific training progress remains unclear. In addition, Kimi K3.1 will be released before mid-October, ...
According to a Nomura Securities report, Moonshot AI is developing a next-generation large model with 5 to 6 trillion parameters — nearly double the size of Kimi K3 — with a shortage of high-end training compute being the main bottleneck. Meanwhile, Kimi K3.1 is set to launch before mid-October, with a focus on improving agents' ability to execute long-running tasks.
Kimi K3: Open Frontier Intelligence
Kimi introduces Kimi K3, a 2.8 trillion parameter open model with native vision and 1M context, built on Kimi Delta Attention and Attention Residuals. It achieves frontier-level performance in coding and reasoning, with full weights to be released by July 2026.
How Kimi K3 Engineered Its Way to the Frontier [R]
Kimi K3 by Moonshot is an open-weight model ranking fourth among 580 models, featuring innovations like Kimi Delta Attention to reduce KV cache memory, Quantile Balancing for expert load balancing, and AgentENV for efficient RL training sandboxing.