# Guide for Running Multiple AI Services on a Single GPU with VRAM Management

## Introduction
Running multiple AI services on a single GPU can be challenging due to limited VRAM resources. This guide covers techniques such as vLLM tensor parallelism, model unloading, memory optimization, and service prioritization to efficiently manage GPU resources.

## vLLM Tensor Parallelism
vLLM (Vectorized Large Language Model) is designed to optimize the execution of large language models on GPUs. Tensor parallelism splits the model's tensors across multiple GPUs, but it can also be adapted to maximize the utilization of a single GPU.

### Steps to Enable Tensor Parallelism on a Single GPU:
1. **Install vLLM**: Ensure you have vLLM installed in your environment.
   ```bash
   pip install vllm
   ```
2. **Configure Model Sharding**: Configure the model to shard tensors across the available GPU memory.
   ```python
   from vllm import LLM, SamplingParams
   llm = LLM(model='your-model', tensor_parallel_size=1)  # Adjust based on your GPU capacity
   ```
3. **Optimize Model Execution**: Use vLLM's execution optimizations to improve performance.
   ```python
   sampling_params = SamplingParams(temperature=0.7)
   outputs = llm.generate(inputs=['Your input text'], sampling_params=sampling_params)
   ```

## Model Unloading
Unloading models that are not currently in use can free up VRAM for other services.

### Steps to Unload Models:
1. **Unload Unused Models**: Implement logic to unload models when they are not needed.
   ```python
   del model  # Delete the model reference
   torch.cuda.empty_cache()  # Free up unused memory
   ```
2. **Automatic Model Management**: Use frameworks that support automatic model loading and unloading based on demand.
   ```python
   from transformers import AutoModelForCausalLM, AutoTokenizer
   tokenizer = AutoTokenizer.from_pretrained('your-model')
   model = AutoModelForCausalLM.from_pretrained('your-model')
   # Load and unload models dynamically as required
   ```

## Memory Optimization
Optimizing memory usage can significantly extend the number of models you can run on a single GPU.

### Techniques for Memory Optimization:
1. **Use Mixed Precision**: Utilize mixed precision training to reduce memory usage.
   ```python
   from torch.cuda.amp import autocast
   with autocast():
       outputs = model(inputs)
   ```
2. **Gradient Checkpointing**: Enable gradient checkpointing to save memory during training.
   ```python
   model.gradient_checkpointing_enable()
   ```
3. **Batch Size Reduction**: Reduce the batch size to fit more models in memory.
   ```python
   outputs = model.generate(inputs, max_length=50, num_return_sequences=1)
   ```

## Service Prioritization
Prioritizing services ensures that critical applications get the necessary GPU resources.

### Strategies for Service Prioritization:
1. **Resource Allocation**: Allocate GPU resources based on service priority.
   ```bash
   # Example: Using CUDA_VISIBLE_DEVICES to control GPU visibility
   CUDA_VISIBLE_DEVICES=0 python high_priority_service.py &
   CUDA_VISIBLE_DEVICES=0 python low_priority_service.py --low-priority
   ```
2. **Dynamic Resource Management**: Implement dynamic resource management to adjust allocations based on demand.
   ```python
   import psutil
   gpu_memory_usage = psutil.virtual_memory().percent
   # Adjust resource allocation based on memory usage
   ```
3. **Service Queuing**: Queue services to run based on priority.
   ```python
   import queue
   service_queue = queue.PriorityQueue()
   service_queue.put((priority_level, service_function))
   # Process services based on priority
   ```

## Conclusion
By leveraging vLLM tensor parallelism, model unloading, memory optimization, and service prioritization, you can effectively run multiple AI services on a single GPU. Proper management of VRAM resources is crucial for maximizing the efficiency and performance of your AI deployments.

---
**References:**
- [vLLM Documentation](https://github.com/vllm-project/vllm)
- [PyTorch Mixed Precision Training](https://pytorch.org/docs/stable/notes/amp.html)
- [CUDA_VISIBLE_DEVICES](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#envvars)

Feel free to customize and extend this guide based on your specific requirements and environment.