Liger-Kernel

Open Source

LLM Inference Updated Feb 15, 2026

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Compared with 16 other LLM Inference agents Open source Self-hostable Updated Feb 2026

💰 Open Source 🔄 Updated Feb 2026 🖥️ Self-hostable

💰 Pricing

Open Source

Pricing not publicly listed.

🎯 Use Cases

Accelerating LLM training pipelines Optimizing GPU resource usage for deep learning workloads Reducing training time for large-scale language models Enhancing Triton-based inference deployments

⚖️ Pros & Cons

✅ Pros

Open-source and self-hostable
Optimized for GPU performance
Integrates with Triton for efficient LLM workloads

❌ Cons

Requires technical expertise to deploy and optimize
Limited to Triton-compatible environments
May need customization for specific use cases

Overview

Liger-Kernel provides optimized Triton kernels for efficient large language model (LLM) training, focusing on performance improvements for GPU-accelerated workloads. It is designed to enhance the speed and resource utilization of LLM training pipelines.

Problem It Solves

It reduces computational overhead and improves efficiency in LLM training by offering highly optimized GPU kernels.

Target Audience: Developers and teams working with llm inference automation.

Inputs

• User configuration
• API credentials (if required)
• Task parameters

Outputs

• Automated task results
• Status reports
• Generated content or actions

Example Workflow

1 User configures the agent with required parameters
2 Agent receives input data or trigger
3 Agent processes the request using its core logic
4 Agent interacts with external services if needed
5 Results are returned to the user

Sample System Prompt


              You are Liger-Kernel, an AI assistant. Help the user accomplish their task efficiently.

Tools & Technologies

LLM APIs Python

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FAQs

Is this agent open-source?: Yes
Can this agent be self-hosted?: Yes
What skill level is required?: Intermediate

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