Kimi K3 Ships Without K2's Modified-MIT License

Moonshot released Kimi K3's weights on July 27, a 2.8-trillion-parameter model that drops the modified-MIT license its predecessor K2 shipped under, per the model card.

Any company running K3 as a paid service must sign a separate agreement with Moonshot. That requirement kicks in once revenue crosses $20 million over any 12-month span, per the model card. That number should shape a product team's build decision, not the trillion-parameter headline.

The model activates 104 billion of its 2.8 trillion parameters per token across 896 routed experts. It runs 93 layers split between Kimi Delta Attention and Gated MLA, with a 1,048,576-token context window. It shipped as MXFP4 weights trained quantization-aware, not squeezed down after the fact, per Willison's rundown of the Hugging Face repo.

The license is labeled 'open weight,' not open source, and Willison flagged three secondary write-ups mislabeling K3 as Apache 2.0, calling them wrong. K2's requirement to credit Moonshot in consumer products is gone in K3.

Good luck running it

An r/LocalLLaMA deployment team worked the hardware math first. 8x A100 80GB gives 640GB against 1.4TB of weights, three nodes before a byte of KV cache. Ampere has no FP4 or FP8 tensor cores. 8x H200 hits about 1.13TB, still two nodes. Only 8x B300, at 2.3TB, fits the model on one node, the hardware Moonshot quantized for.

vLLM shipped day-0 support in v0.26.0, hitting 118 tokens per second per user baseline. DSpark speculative decoding pushed that to 370 tokens per second on 16x GB300 NVL72, a 3.14x jump. It runs only in Docker, since the build needs a pre-release FlashInfer.

No GGUF quant of K3 loads on llama.cpp. The thread tracking its support lists a missing architecture registration, an unimplemented activation function, and unhandled tensors for its latent expert space. Builds from GrEarl, Kuberwastaken, and AtomicChat all fail to load.

Moonshot's tech report claims Quantile Balancing and Per-Head Muon tuning give K3 2.5 times better compute-to-capability than K2. The hfviewer Expert Atlas post found K3's active parameter fraction is 1.8%, the smallest yet on the largest expert pool shipped in the open.