EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·Jul 31·5 min read
Why we write our own C and C++ inference engines
TL;DR
LocalAI developed custom C and C++ inference engines to avoid the overhead of large Python dependencies and improve deployment efficiency. These ports maintain competitive performance while significantly reducing memory usage.
✦ Why It Matters
Consider developing C/C++ ports for models with heavy Python dependencies to streamline deployment.
Key Takeaways
- Evaluate models for potential C/C++ porting to reduce dependencies.
- Use GGUF files for easier model deployment.
- Benchmark performance against existing Python implementations.
- Focus on memory efficiency to improve deployment predictability.
How It Works
LocalAI's ports convert model weights into a single GGUF file, allowing for straightforward deployment. The graph is then ported and validated against reference outputs to ensure accuracy.
Performance optimizations are applied only after confirming output parity, which helps identify inefficiencies in the original Python implementations.