# Kimi K3 (2.8T)

> 2.8T sparse hybrid that combines a 3:1 KDA and Gated MLA stack with Block Attention Residuals and Stable LatentMoE. It is also native multimodal, but the figure focuses on the text portion.

| Property | Value |
| --- | --- |
| Lab | Moonshot AI (China) |
| Released | 2026-07-27 |
| Decoder type | Sparse hybrid |
| Family | Hybrid MoE |
| Scale | 2.8T total, 104B active (3.7% active) |
| Context window | 1,048,576 tokens |
| Attention | 3:1 Kimi Delta Attention and Gated MLA with NoPE |
| Layer mix | 69 KDA + 24 Gated MLA |
| KV cache / token (bf16) | 27 KiB (Low) |
| License | Kimi K3 License |

**Key detail:** Uses eight Block Attention Residual groups and Stable LatentMoE with 16 of 896 routed experts active per token.

## Architecture concepts

- [No Positional Embeddings (NoPE)](https://llm-atlas-7or.pages.dev/concepts/nope.md): Some or all layers drop explicit positional encodings and rely on the causal mask.
- [Multi-Head Latent Attention (MLA)](https://llm-atlas-7or.pages.dev/concepts/mla.md): Keys and values are compressed into a small latent vector that is cached instead of full K/V.
- [Gated Attention (Gated attention)](https://llm-atlas-7or.pages.dev/concepts/gated-attention.md): A learned sigmoid gate scales the attention output before it re-enters the residual stream.
- [Hybrid Attention (Hybrid attention)](https://llm-atlas-7or.pages.dev/concepts/hybrid-attention.md): Mixes cheap linear-attention or state-space layers with a few full-attention layers.
- [Short Convolution (ShortConv)](https://llm-atlas-7or.pages.dev/concepts/shortconv.md): A tiny causal depthwise convolution (kernel ≈ 4) that mixes each channel over the last few tokens.
- [Attention Residuals (AttnRes)](https://llm-atlas-7or.pages.dev/concepts/attention-residuals.md): Learned, attention-style weights over earlier layer outputs replace the plain residual sum.
- [Latent MoE (Latent MoE)](https://llm-atlas-7or.pages.dev/concepts/latent-moe.md): Routed experts operate in a smaller latent space instead of the full hidden width.
- [Mixture of Experts (MoE)](https://llm-atlas-7or.pages.dev/concepts/moe.md): Many feed-forward experts, but a router sends each token to only a few of them.

## Benchmark (Artificial Analysis Intelligence Index)

- AA Intelligence Index v4.3.2: total 43.6 (General 45.8, Scientific 35.2, Coding 36.1, Agents 51.9)

## Links

- Architecture diagram: https://sebastianraschka.com/llm-architecture-gallery/images/architectures/kimi-k3.webp
- config.json: https://huggingface.co/moonshotai/Kimi-K3/blob/main/config.json
- License: https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE
- Tech report: https://github.com/MoonshotAI/Kimi-K3/blob/main/k3_tech_report.pdf
- Source card (source of truth): https://sebastianraschka.com/llm-architecture-gallery/#card-kimi-k3
- Web page: https://llm-atlas-7or.pages.dev/models/kimi-k3/
- JSON: https://llm-atlas-7or.pages.dev/api/models/kimi-k3.json

_Data from Sebastian Raschka's LLM Architecture Gallery (https://sebastianraschka.com/llm-architecture-gallery/). Synced 2026-11-04._