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Miles supports Moonshot’s MoE line from top to bottom. The latest Kimi K2.6 and K2.5 are natively multimodal agentic models at 1 T total / 32 B active per token; the text-only Kimi K2 (Instruct and Thinking variants) runs at the same 1 T / 32 B scale; and the compact Moonlight 16B-A3B fits on a single 8× H100 node, a handy single-node test target before scaling K2 across many nodes. K2-Thinking is the canonical INT4 QAT target, and the K2.5 / K2.6 recipe trains an INT4 actor under the same QAT path.

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Variants

ModelActive / TotalHF IDRecipe
Kimi-K2.632 B / 1 Tmoonshotai/Kimi-K2.6kimi-k2.5
Kimi-K2.532 B / 1 Tmoonshotai/Kimi-K2.5kimi-k2.5
Kimi-K2-Instruct32 B / 1 Tmoonshotai/Kimi-K2-Instructkimi-k2
Kimi-K2-Thinking32 B / 1 Tmoonshotai/Kimi-K2-Thinkingkimi-k2
Moonlight-16B-A3B3 B / 16 Bmoonshotai/Moonlight-16B-A3Bmoonlight

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Fastest path to train

Moonlight on a single 8× H100 node — the smallest Moonshot recipe and a good MoE smoke test: 
cd /root/miles
hf download moonshotai/Moonlight-16B-A3B --local-dir /root/Moonlight-16B-A3B
bash scripts/run-moonlight-16B-A3B.sh
See the Moonlight page for the full walkthrough, or Kimi K2 for the 16-node K2-Thinking recipe (including the one-line model_type patch that lets Miles treat K2 as a DeepSeek-V3-shaped architecture).

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Which variant do I pick?

  • Latest multimodal agentic model → Kimi-K2.6 or Kimi-K2.5 (kimi-k2.5).
  • Single-node MoE smoke test → Moonlight-16B-A3B (moonlight).
  • Frontier-scale instruction-tuned MoE → Kimi-K2-Instruct (kimi-k2).
  • Reasoning-style training, INT4 QAT target → Kimi-K2-Thinking (kimi-k2).