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README.md
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@@ -52,7 +52,40 @@ This is an **ultra-compact 2-bit quantized version** of Kimi K2 Instruct, optimi
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## 🎯 Quick Start
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```bash
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pip install mlx-lm
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## 🎯 Quick Start
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## Hardware Requirements
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Kimi-K2 is a massive 671B parameter MoE model. Choose your quantization based on available unified memory:
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| Quantization | Model Size | Min RAM | Quality |
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|:------------:|:----------:|:-------:|:--------|
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| **2-bit** | ~84 GB | 96 GB | Acceptable - some quality loss |
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| **3-bit** | ~126 GB | 128 GB | Good - recommended minimum |
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| **4-bit** | ~168 GB | 192 GB | Very Good - best quality/size balance |
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| **5-bit** | ~210 GB | 256 GB | Excellent |
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| **6-bit** | ~252 GB | 288 GB | Near original |
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| **8-bit** | ~336 GB | 384 GB | Original quality |
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### Recommended Configurations
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| Mac Model | Max RAM | Recommended Quantization |
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|:----------|:-------:|:-------------------------|
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| Mac Studio M2 Ultra | 192 GB | 4-bit |
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| Mac Studio M4 Ultra | 512 GB | 8-bit |
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| Mac Pro M2 Ultra | 192 GB | 4-bit |
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| MacBook Pro M3 Max | 128 GB | 3-bit |
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| MacBook Pro M4 Max | 128 GB | 3-bit |
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### Performance Notes
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- **Inference Speed**: Expect ~5-15 tokens/sec depending on quantization and hardware
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- **First Token Latency**: 10-30 seconds for model loading
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- **Context Window**: Full 128K context supported
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- **Active Parameters**: Only ~37B parameters active per token (MoE architecture)
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## Installation
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```bash
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pip install mlx-lm
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