I Built a Modular LLM Inference Engine from Scratch — Here’s What I Learned
📰 Medium · Data Science
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 existing frameworks as a starting point
- Compare the capabilities of vLLM, TensorRT-LLM, and llama.cpp to identify gaps in their functionality
- Design and implement a custom solution, such as inferx, to fill the gaps and improve performance
- Test and evaluate the custom solution using benchmarking and metrics
- Optimize and refine the custom solution for better results
Who Needs to Know This
Data scientists and machine learning engineers can benefit from this knowledge to improve their LLM inference capabilities and create more efficient models
Key Insight
💡 Existing LLM inference engines like vLLM, TensorRT-LLM, and llama.cpp have limitations, and building a custom modular solution can help fill the gaps and improve performance
Share This
🤖 Built a modular LLM inference engine from scratch! 🚀 Learn how to fill the gaps left by existing solutions and improve your LLM capabilities #LLM #InferenceEngine #AI
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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