The Fastest Way to Serve Open-Source Models: Inference Engine 2.0

Base model inference speed might seem like a solved problem—but when you actually deploy open-source models in production, the difference between “it works” and “it performs” becomes painfully clear.

Watch this webinar to see the latest benchmarks from the Predibase Inference Engine 2.0, our latest release that sets a new bar for LLM serving performance. Whether you’re running base models or fine-tuned variants, we’ll show how our optimized stack outperforms out-of-the-box solutions like Fireworks and vLLM—without the guesswork, the tuning, or the headaches.

What you’ll learn:

• Why common open-source inference stacks slow down in real-world conditions
• How Predibase delivers best-in-class performance for both base and fine-tuned models, automatically
• What makes fine-tuned inference uniquely challenging—and how we solve it with speculative decoding, quantization, and autoscaling built in
• Benchmark results across summarization, classification, and chat workloads on real hardware (L40S, H100), including how to reproduce them

If speed, scale, and simplicity matter to your team, join us to see why the fastest way to serve open-source models is with Predibase.