I Built a Modular LLM Inference Engine from Scratch — Here’s What I Learned
📰 Medium · Python
Learn how to build a modular LLM inference engine from scratch and fill the gaps left by existing solutions like vLLM, TensorRT-LLM, and llama.cpp
Action Steps
- Build a modular LLM inference engine using Python
- Compare the performance of vLLM, TensorRT-LLM, and llama.cpp
- Configure the inferx engine to optimize LLM inference
- Test the inferx engine with different LLM models
- Apply the lessons learned to improve existing LLM inference solutions
Who Needs to Know This
Machine learning engineers and researchers can benefit from this knowledge to improve their LLM inference capabilities and create more efficient models. This can also be useful for software engineers working on AI-related projects
Key Insight
💡 Existing LLM inference solutions like vLLM, TensorRT-LLM, and llama.cpp only solve part of the problem, and a modular approach is needed to fill the gap
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🚀 Just built a modular LLM inference engine from scratch! 🤖 Learn how to fill the gaps left by existing solutions like vLLM, TensorRT-LLM, and llama.cpp #LLM #AI #MachineLearning
Key Takeaways
Learn how to build a modular LLM inference engine from scratch and fill the gaps left by existing solutions like vLLM, TensorRT-LLM, and llama.cpp
Full Article
Why vLLM, TensorRT-LLM, and llama.cpp each solve only part of the problem — and how I built inferx to fill the gap Continue reading on Medium »
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