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ktransformers/doc/en
ErvinXie 71f683acec Support Native Kimi K2 Thinking (#1663)
* [feat]: fix k2 prefill

* Update Kimi-K2-Thinking.md

* Create Kimi-K2-Thinking-Native.md

* Update Kimi-K2-Thinking.md

* Update Kimi-K2-Thinking.md

* Update Kimi-K2-Thinking-Native.md

* [perf] optimize K2 MoE weight loading with per-expert pointers

- Avoid expensive torch.stack().contiguous() in Python (was ~6.6s)
- Use per-expert pointer arrays (gate_projs) instead of contiguous memory
- C++ worker pool performs parallel memcpy for TP slicing
- Add LOAD_TIME_PROFILE for load_weights timing analysis

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

---------

Co-authored-by: ouqingliang <1692110604@qq.com>
Co-authored-by: Claude <noreply@anthropic.com>
2025-12-05 21:53:05 +08:00
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