I Built a Modular LLM Inference Engine from Scratch — Here’s What I Learned
📰 Medium · Programming
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
- Run performance benchmarks on vLLM, TensorRT-LLM, and llama.cpp to identify gaps
- Configure and optimize the engine for specific use cases and hardware
- Test the engine with various LLM models and datasets
- Apply the lessons learned to improve the engine's efficiency and scalability
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 engines like vLLM, TensorRT-LLM, and llama.cpp only solve part of the problem, and building a modular engine from scratch can help fill the gaps and improve efficiency.
Share This
🤖 Built a modular LLM inference engine from scratch! 🚀 Learn how to fill the gaps left by existing solutions and improve your LLM inference capabilities.
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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