multi-gpu

Comprehensive guide to scaling TensorRT-LLM across multiple GPUs and nodes.

Skills
AI research
2

What it does and how to use it

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Install (Claude Code)

mkdir -p .claude/skills/multi-gpu && curl -fsSL https://doutorai.pt/data/lib/skills__multi-gpu.md -o .claude/skills/multi-gpu/SKILL.md

Paste this command into the terminal, in your project folder, and the component installs automatically.

Content

# Multi-GPU Deployment Guide

Comprehensive guide to scaling TensorRT-LLM across multiple GPUs and nodes.

## Parallelism Strategies

### Tensor Parallelism (TP)

**What it does**: Splits model layers across GPUs horizontally.

**Use case**:
- Model fits in total GPU memory but not single GPU
- Need low latency (single forward pass)
- GPUs on same node (NVLink required for best performance)

**Example** (Llama 3-70B on 4× A100):
```python
from tensorrt_llm import LLM

llm = LLM(
    model="meta-llama/Meta-Llama-3-70B",
    tensor_parallel_size=4,  # Split across 4 GPUs
    dtype="fp16"
)

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