llamatelemetry

I have built Llamatelemetry after working deeply with local LLM systems using llama.cpp and CUDA. While I could measure performance in small ways, I realized something important was missing: proper observability. Cloud AI platforms provide dashboards and tracing, but when running models locally on GPUs, there was no clean, standardized way to understand what was happening inside the system. That gap inspired me to create a Python SDK that brings structured telemetry and tracing to local LLM inference.

Llamatelemetry connects llama.cpp inference with OpenTelemetry so developers can see how their models behave in real time. It tracks how long prompts take to process, how fast tokens are generated, and how the overall request performs. The goal is simple: if you are running AI locally, you should be able to monitor it like a production system, not like a black box experiment.

While building this project, I learned that observability is often more complex than the model itself. It requires careful design to keep things simple for developers while still capturing meaningful data. I also had to align the SDK with evolving OpenTelemetry standards so that it stays compatible with the broader ecosystem instead of becoming another isolated tool.

The biggest challenge was balancing power and simplicity. I wanted developers to add observability with minimal effort, but I also wanted the system to provide deep insight when needed. Llamatelemetry represents my step toward making local AI systems more reliable, measurable, and ready for real-world production use.

Built With

• c++
• cuda
• gguf
• grafana
• graphistry
• llama.cpp
• opentelemetry
• python
• unsloth