Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
Data Science
Towards Data Science·Jul 22·5 min read
How To Build Your Own LLM Runtime From Scratch
TL;DR
A custom LLM inference runtime called annotated-llm-runtime was built on an H100 GPU, showcasing the process of managing weights and CUDA graphs. Key challenges included resolving three significant bugs that impacted annotations.
✦ Why It Matters
Start building your own LLM runtime today to gain deeper control over model performance and deployment.
Key Takeaways
- Explore building custom LLM runtimes for tailored optimizations.
- Utilize CUDA graphs to minimize kernel launch overhead.
- Ensure proper synchronization to avoid undefined behavior.
- Use llama.cpp as a benchmark for performance comparison.
How It Works
The runtime employs CUDA graphs to capture and replay kernel executions, significantly reducing the overhead associated with multiple kernel launches. By pre-capturing different execution paths based on token positions, the runtime can efficiently handle memory page boundaries, optimizing the decoding process.