Predibase has launched its Inference Engine 2.0, which enhances the deployment of large language models (LLMs) by improving efficiency, throughput, and GPU performance, while reducing infrastructure costs. The engine introduces optimizations such as Turbo-Charged Inference, Multi-Turbo Inference, and integration of chunked prefill with speculative decoding, improving support for embeddings and classification models. Real-world benchmarking against Fireworks and vLLM demonstrated Predibase's superior performance, offering up to four times faster inference speeds with sustained high performance under heavy loads. The engine's design incorporates proprietary techniques like Turbo LoRA for speculative decoding, ensuring comprehensive optimization out of the box without manual configuration. The benchmarks highlighted Predibase's consistent low latency and scalability, positioning it as a leading inference platform for production LLM workloads. Additionally, the platform emphasizes the importance of a managed, end-to-end inference solution over raw speed alone, advocating for intelligent optimization and streamlined infrastructure management.