optimization

Comprehensive guide to optimizing LLM inference with TensorRT-LLM.

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Content

# TensorRT-LLM Optimization Guide

Comprehensive guide to optimizing LLM inference with TensorRT-LLM.

## Quantization

### FP8 Quantization (Recommended for H100)

**Benefits**:
- 2× faster inference
- 50% memory reduction
- Minimal accuracy loss (<1% perplexity degradation)

**Usage**:
```python
from tensorrt_llm import LLM

# Automatic FP8 quantization
llm = LLM(
    model="meta-llama/Meta-Llama-3-70B",
    dtype="fp8",
    quantization="fp8"
)
```

**Performance** (Llama 3-70B on 8× H100):
- FP16: 5,000 tokens/sec
- FP8: **10,000 tokens/sec** (2× speedup)
- Memory: 140GB → 70GB

### INT4 Q

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