Tips & Tricks: a Self-Hosted LLM

Understand Your Needs

While it’s tempting to use the largest models with a large number of parameters for additional capabilities and the most accurate responses, they are extremely compute-intensive and may offer capabilities beyond your requirements. Assess your needs carefully to avoid unnecessary resource consumption.

Right-Size Your Machine

Running self-hosted LLMs can be expensive. Ensure you are using appropriately sized machines, especially GPUs with the right amount of VRAM based on the number of parameters.

Continues Monitoring

Monitor your LLM’s behaviour not just for failures, but also for key metrics (e.g., tokens per second, requests per second, latency). This will help you optimize your environment effectively.

Leverage Open Source

Utilize open-source tools like KEDA, vLLM, Spegel, LoRA, and others. These tools can help with event-driven autoscaling, handling high loads and concurrent requests, reducing download speeds, and more.

Optimize Performance

Use auto-scaling to save costs and enhance performance. Reduce download times for images and weights, select the right LLM for your specific use case, and continuously monitor metrics to make necessary adjustments.

Thank you for reading!