linkedin/Liger-Kernel

Health v2 · maintenance only

Adoption not part of the score

6588 stars · 586 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Liger Kernel is a collection of Triton kernels designed to make LLM training faster and more memory-efficient, offering drop-in replacements for layers like RMSNorm, RoPE, SwiGLU, and fused cross-entropy. It patches Hugging Face models with one line of code, boosting multi-GPU training throughput by ~20% and cutting memory usage by up to 60%, with additional kernels for post-training losses like DPO and ORPO.

Use cases

• speed up multi-GPU LLM fine-tuning throughput
• reduce GPU memory usage when training large language models
• train LLaMA or Mistral models with longer context lengths without OOM
• optimize post-training alignment losses like DPO and ORPO
• patch Hugging Face models with efficient fused kernels
• enable larger batch sizes and massive vocabularies during training

When to choose

• you are fine-tuning supported LLMs (LLaMA, Mistral, Gemma, Phi) on NVIDIA or AMD GPUs
• you hit OOM errors during training and need memory savings without changing model behavior
• you use PyTorch FSDP, DeepSpeed, or Flash Attention and want compatible kernel optimizations
• you need memory-efficient post-training losses for alignment or distillation

When to avoid

• you are not training or fine-tuning LLMs (e.g., inference-only or non-transformer workloads)
• your model architecture is not yet supported by Liger's kernel set
• you need a pure PyTorch implementation without Triton or GPU dependencies
• you are training on CPUs or non-GPU hardware

Facets

library · maturity active

llm-training gpu-computing machine-learning deep-learning large-language-models machine-learning gpu-computing deep-learning python triton-kernels finetuning memory-optimization huggingface fused-kernels post-training flash-attention gpu linux

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markdown · JSON · MCP: product_card(name="linkedin/Liger-Kernel")