# How to Change KV Cache Behavior

Set KV cache behavior by providing the optional ```kv_cache_config argument``` when you create the LLM engine. Consider the quickstart example found in ```examples/pytorch/quickstart.py```:

```python
from tensorrt_llm import LLM, SamplingParams


def main():
    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]
    sampling_params = SamplingParams(max_tokens=32)

    llm = LLM(model='TinyLlama/TinyLlama-1.1B-Chat-v1.0')
    outputs = llm.generate(prompts, sampling_params)

    for i, output in enumerate(outputs):
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"[{i}] Prompt: {prompt!r}, Generated text: {generated_text!r}")


if __name__ == '__main__':
    main()
```

This example runs with default KV cache properties. The default for `free_gpu_memory_fraction` is 0.9, which means TensorRT LLM will try to allocate 90% of free GPU memory for KV cache. Depending on your system, this may be too aggressive, so you decide to dial that back to 0.7. This is done by adding the following lines to the quickstart example:

```python
from tensorrt_llm.llmapi import KvCacheConfig
kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.7)
llm = LLM(model='TinyLlama/TinyLlama-1.1B-Chat-v1.0', kv_cache_config=kv_cache_config)
```

You can also set properties after you create ```KvCacheConfig```. For example:

```python
kv_cache_config = KvCacheConfig()
kv_cache_config.enable_block_reuse = False
llm = LLM(model='TinyLlama/TinyLlama-1.1B-Chat-v1.0', kv_cache_config=kv_cache_config)
```

This code disables block reuse for the quick start example.