NCCL and PyTorch¶

Understanding NCCL vs tensor-split for distributed workloads.

Level: Advanced | Time: 25 minutes | VRAM Required: 15-25 GB

Key Differences¶

llama-server tensor-split: - Native CUDA layer distribution - NO NCCL required - For LLM inference

PyTorch DDP with NCCL: - Distributed training - Requires NCCL - For fine-tuning

llama-server (NO NCCL)¶

from llcuda.server import ServerConfig

config = ServerConfig(
    model_path="model.gguf",
    tensor_split="0.5,0.5",  # Native CUDA split
    n_gpu_layers=99
)

PyTorch DDP (Uses NCCL)¶

import torch.distributed as dist

dist.init_process_group(backend="nccl")

# DDP training code here

Open in Kaggle¶