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+# DeepSeek-Coder-V2 Quantization Guide
+
+This guide provides comprehensive information on how to quantize DeepSeek-Coder-V2 models for efficient inference with various frameworks including vLLM, SGLang, and other popular inference engines.
+
+## Table of Contents
+1. [Overview](#overview)
+2. [Supported Quantization Methods](#supported-quantization-methods)
+3. [Quantization with vLLM](#quantization-with-vllm)
+4. [Quantization with SGLang](#quantization-with-sglang)
+5. [Quantization with llama.cpp](#quantization-with-llamacpp)
+6. [Quantization with AutoGPTQ](#quantization-with-autogptq)
+7. [Performance Comparison](#performance-comparison)
+8. [Troubleshooting](#troubleshooting)
+
+## Overview
+
+DeepSeek-Coder-V2 models can be quantized to reduce memory usage and improve inference speed while maintaining high code generation quality. The models support various quantization methods depending on your inference framework and requirements.
+
+## Supported Quantization Methods
+
+### Model Variants
+- **DeepSeek-Coder-V2-Lite-Base** (16B total, 2.4B active parameters)
+- **DeepSeek-Coder-V2-Lite-Instruct** (16B total, 2.4B active parameters)
+- **DeepSeek-Coder-V2-Base** (236B total, 21B active parameters)
+- **DeepSeek-Coder-V2-Instruct** (236B total, 21B active parameters)
+
+### Quantization Levels
+- **FP16/BF16**: Full precision (recommended for maximum quality)
+- **INT8**: 8-bit quantization (good balance of speed and quality)
+- **INT4**: 4-bit quantization (maximum compression, some quality loss)
+- **FP8**: 8-bit floating point (supported by SGLang)
+
+## Quantization with vLLM
+
+### Prerequisites
+```bash
+# Install vLLM with DeepSeek-Coder-V2 support
+pip install vllm
+# Note: You need to merge PR #4650 for full support
+# https://github.com/vllm-project/vllm/pull/4650
+```
+
+### Basic Quantization Example
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+
+# Load model with quantization
+model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Quantize to INT8
+llm = LLM(
+    model=model_name,
+    tensor_parallel_size=1,
+    max_model_len=8192,
+    trust_remote_code=True,
+    enforce_eager=True,
+    quantization="int8"  # or "int4", "fp16", "bf16"
+)
+
+# Use the model
+sampling_params = SamplingParams(
+    temperature=0.3, 
+    max_tokens=256, 
+    stop_token_ids=[tokenizer.eos_token_id]
+)
+
+messages = [{"role": "user", "content": "Write a Python function to calculate fibonacci numbers."}]
+prompt_token_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
+outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
+print(outputs[0].outputs[0].text)
+```
+
+### Advanced vLLM Quantization Options
+```python
+# INT4 quantization with specific settings
+llm = LLM(
+    model=model_name,
+    tensor_parallel_size=2,
+    max_model_len=8192,
+    trust_remote_code=True,
+    enforce_eager=True,
+    quantization="int4",
+    gpu_memory_utilization=0.9,
+    max_num_batched_tokens=4096
+)
+```
+
+## Quantization with SGLang
+
+SGLang provides excellent support for DeepSeek-Coder-V2 with built-in quantization optimizations.
+
+### FP8 Quantization (Recommended)
+```bash
+# Install SGLang
+pip install sglang
+
+# Launch server with FP8 quantization
+python3 -m sglang.launch_server \
+    --model neuralmagic/DeepSeek-Coder-V2-Instruct-FP8 \
+    --tp 8 \
+    --trust-remote-code \
+    --kv-cache-dtype fp8_e5m2
+```
+
+### BF16 with Torch Compile
+```bash
+# BF16 with compilation for better performance
+python3 -m sglang.launch_server \
+    --model deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct \
+    --trust-remote-code \
+    --enable-torch-compile
+```
+
+### Python Client Example
+```python
+import openai
+
+client = openai.Client(
+    base_url="http://127.0.0.1:30000/v1", 
+    api_key="EMPTY"
+)
+
+response = client.chat.completions.create(
+    model="default",
+    messages=[
+        {"role": "system", "content": "You are a helpful coding assistant."},
+        {"role": "user", "content": "Write a quicksort implementation in Python."},
+    ],
+    temperature=0,
+    max_tokens=512,
+)
+print(response.choices[0].message.content)
+```
+
+## Quantization with llama.cpp
+
+For CPU inference or maximum compatibility, you can use llama.cpp with GGUF format.
+
+### Convert to GGUF Format
+```bash
+# Install llama.cpp
+git clone https://github.com/ggerganov/llama.cpp
+cd llama.cpp
+make
+
+# Convert model to GGUF format
+python3 convert.py deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct \
+    --outfile deepseek-coder-v2-lite-instruct.gguf \
+    --outtype q4_0
+```
+
+### Quantization Levels Available
+- `q4_0`: 4-bit quantization (recommended)
+- `q4_1`: 4-bit quantization with improved accuracy
+- `q5_0`: 5-bit quantization
+- `q5_1`: 5-bit quantization with improved accuracy
+- `q8_0`: 8-bit quantization
+
+### Inference with llama.cpp
+```bash
+# Run inference with quantized model
+./main -m deepseek-coder-v2-lite-instruct.gguf \
+    -n 512 \
+    -p "Write a Python function to sort a list:" \
+    --repeat_penalty 1.1
+```
+
+## Quantization with AutoGPTQ
+
+For GPU inference with GPTQ quantization.
+
+### Installation
+```bash
+pip install auto-gptq
+pip install transformers
+```
+
+### Quantization Example
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+
+# Load model and tokenizer
+model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+
+# Configure quantization
+quantize_config = BaseQuantizeConfig(
+    bits=4,  # 4-bit quantization
+    group_size=128,
+    desc_act=False,
+)
+
+# Quantize the model
+model = AutoGPTQForCausalLM.from_pretrained(
+    model_name,
+    quantize_config=quantize_config,
+    trust_remote_code=True,
+    device_map="auto"
+)
+
+# Save quantized model
+model.save_quantized("deepseek-coder-v2-lite-instruct-gptq")
+
+# Load and use quantized model
+quantized_model = AutoGPTQForCausalLM.from_quantized(
+    "deepseek-coder-v2-lite-instruct-gptq",
+    device_map="auto",
+    trust_remote_code=True
+)
+```
+
+## Performance Comparison
+
+| Quantization Method | Memory Usage | Speed | Quality | Framework |
+|-------------------|-------------|-------|--------|-----------|
+| FP16/BF16 | 100% | 100% | 100% | All |
+| INT8 | ~50% | ~120% | ~95% | vLLM, SGLang |
+| INT4 | ~25% | ~150% | ~90% | vLLM, llama.cpp |
+| FP8 | ~50% | ~130% | ~98% | SGLang |
+| GPTQ-4bit | ~25% | ~140% | ~88% | AutoGPTQ |
+
+*Note: Performance metrics are approximate and may vary based on hardware and specific use cases.*
+
+## Memory Requirements
+
+### GPU Memory (per GPU)
+- **FP16/BF16**: 
+  - Lite models: ~32GB
+  - Full models: ~84GB
+- **INT8**: 
+  - Lite models: ~16GB
+  - Full models: ~42GB
+- **INT4**: 
+  - Lite models: ~8GB
+  - Full models: ~21GB
+
+### CPU Memory
+- **GGUF q4_0**: 
+  - Lite models: ~8GB
+  - Full models: ~21GB
+
+## Troubleshooting
+
+### Common Issues
+
+1. **"Model not found" error**
+   ```bash
+   # Ensure you're using the correct model name
+   model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
+   ```
+
+2. **Out of memory errors**
+   ```python
+   # Reduce tensor parallelism or use quantization
+   llm = LLM(
+       model=model_name,
+       tensor_parallel_size=1,  # Reduce from 8 to 1
+       quantization="int8"
+   )
+   ```
+
+3. **vLLM compatibility issues**
+   ```bash
+   # Check if you have the latest vLLM with DeepSeek support
+   pip install --upgrade vllm
+   # Or use the PR branch: https://github.com/vllm-project/vllm/pull/4650
+   ```
+
+4. **Tokenization errors**
+   ```python
+   # Always use trust_remote_code=True
+   tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+   ```
+
+### Performance Optimization Tips
+
+1. **Use tensor parallelism for large models**
+   ```python
+   tensor_parallel_size=8  # For 236B models
+   ```
+
+2. **Enable torch.compile with SGLang**
+   ```bash
+   --enable-torch-compile
+   ```
+
+3. **Optimize batch size**
+   ```python
+   max_num_batched_tokens=4096  # Adjust based on your GPU memory
+   ```
+
+4. **Use appropriate quantization for your use case**
+   - **Maximum quality**: FP16/BF16
+   - **Balanced**: INT8/FP8
+   - **Maximum speed**: INT4
+
+## Additional Resources
+
+- [vLLM Documentation](https://docs.vllm.ai/)
+- [SGLang Documentation](https://docs.sgl.ai/)
+- [llama.cpp Documentation](https://github.com/ggerganov/llama.cpp)
+- [AutoGPTQ Documentation](https://github.com/PanQiWei/AutoGPTQ)
+- [DeepSeek-Coder-V2 Paper](https://arxiv.org/pdf/2406.11931)
+
+## Support
+
+If you encounter issues with quantization, please:
+1. Check the troubleshooting section above
+2. Search existing issues: https://github.com/deepseek-ai/DeepSeek-Coder-V2/issues
+3. Create a new issue with detailed error information
+4. Contact the team at service@deepseek.com
diff --git a/README.md b/README.md
index 7ec8843..d260f5e 100644
--- a/README.md
+++ b/README.md
@@ -188,6 +188,14 @@ We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.c
 ## 6. How to run locally
 **Here, we provide some examples of how to use DeepSeek-Coder-V2-Lite model. If you want to utilize DeepSeek-Coder-V2 in BF16 format for inference, 80GB*8 GPUs are required.**
 
+### Quantization Guide
+For efficient inference with reduced memory usage, see our comprehensive [Quantization Guide](QUANTIZATION_GUIDE.md) which covers:
+- vLLM quantization support
+- SGLang FP8 optimization  
+- llama.cpp GGUF conversion
+- AutoGPTQ integration
+- Performance comparisons and troubleshooting
+
 ### Inference with Huggingface's Transformers
 You can directly employ [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference.