LocalClaw/LLM/Kimi K2 Instruct (1T MoE)
Open-weight local LLM

Kimi K2 Instruct (1T MoE)

Moonshot AI trillion-parameter MoE flagship. 32B active params per token with 384 experts. Matches or beats GPT-4 Turbo on MMLU, GSM8K, HumanEval. Agentic & tool-use specialist. Server-grade only. Modified MIT.

Server-grade 1024 GB RAM Q4_K_M Coding assistant
Run with LocalClaw Compare all models
Parameters
1T (32B active, 384 experts)
Minimum RAM
1024 GB
Model size
600 GB
Quantization
Q4_K_M

Can Kimi K2 Instruct (1T MoE) run locally?

Kimi K2 Instruct (1T MoE) is server-grade locally. Keep it for comparison unless you have very large unified memory, multiple GPUs or remote inference.

Search for kimi-k2-instruct in LM Studio or another GGUF-compatible runtime.

moonshotai/Kimi-K2-Instruct
chatcodereasoningqualitygeneral

Install path

01
Check RAM fitMinimum 1024 GB RAM. Start with the Q4_K_M quant.
02
Load the modelSearch kimi-k2-instruct in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Strengths

  • Moonshot AI trillion-parameter MoE flagship. 32B active params per token with 384 experts. Matches or beats GPT-4 Turbo on MMLU, GSM8K, HumanEval. Agentic & tool-use specialist. Server-grade only. Modified MIT.

Limitations

  • Performance depends on quantization, RAM bandwidth and runtime support.

Best use cases

  • chat
  • code
  • reasoning
  • quality
  • general

Capability profile

speed
3
quality
10
coding
10
reasoning
10

Technical notes

Developer
kimi
License
See model repository
Context window
Unknown tokens
Architecture
See model card

This model fits these next steps

Hardware fit is based on LocalClaw's RAM tier, model size and quantization metadata. Always leave memory headroom for your OS and runtime.

Very large memoryMac Studio Ultra classCheck model size firstNVIDIA GB10 / server optionsMore practical alternativesCompare smaller models

Where to go next