# Local DeepSeek Inference Setup

For maximum speed and no API rate limits, you can run DeepSeek models locally.

## Hardware Requirements

- **GPU**: NVIDIA GPU with 24GB+ VRAM (RTX 4090, A6000, H100)
- **RAM**: 32GB+ system RAM
- **Storage**: 200GB+ free space for model weights

## Installation

```bash
# Install vLLM for fast inference
pip install vllm

# Install additional dependencies
pip install transformers torch
```

## Usage

```python
from vllm import LLM, SamplingParams
import time

# Initialize local model
llm = LLM(
    model="deepseek-ai/deepseek-coder-7b-instruct",  # Smaller model
    # model="deepseek-ai/DeepSeek-V3",  # Full model (requires 80GB+ VRAM)
    tensor_parallel_size=1,  # Use multiple GPUs if available
    gpu_memory_utilization=0.9,
    max_model_len=8192
)

sampling_params = SamplingParams(
    temperature=0.1,
    max_tokens=2000,
    top_p=0.9
)

# Fast batch inference
prompts = ["Your Z3 coding prompt here..."] * 10
start_time = time.time()
outputs = llm.generate(prompts, sampling_params)
end_time = time.time()

print(f"Generated {len(outputs)} responses in {end_time - start_time:.2f} seconds")
# Expected: ~10-50x faster than API calls
```

## Speed Comparison

| Method | 10 Examples | 100 Examples |
|--------|-------------|---------------|
| DeepSeek API | ~5 minutes | ~50 minutes |
| Local GPU (batch) | ~30 seconds | ~5 minutes |
| **Speedup** | **10x faster** | **10x faster** |

## Pros & Cons

**Pros:**
- 10-50x faster than API
- No rate limits
- No API costs
- Batch processing
- Full control

**Cons:**
- Expensive GPU required
- Complex setup
- Large model downloads
- High power consumption 