FriendliAI

What is FriendliAI?

FriendliAI offers Friendli Inference, a cutting-edge generative AI serving engine designed to make large language model (LLM) inference exceptionally fast and cost-effective. By leveraging patented technologies, Friendli Inference achieves 2–5× faster output speed and reduces GPU costs by 50–90%, all while maintaining a 99.99% uptime SLA. The engine is optimized for high throughput and low latency, outperforming competitors like vLLM and TensorRT-LLM in performance tests.

Key features include support for multiple LoRA models on fewer GPUs (even a single GPU), enabling efficient LLM customization. Friendli Inference handles a wide range of models, including quantized models and Mixture of Experts (MoE). The engine utilizes iteration batching, a novel batching technology that efficiently manages concurrent generation requests, achieving up to tens of times higher throughput than conventional batching under the same latency constraints.

Under the hood, Friendli DNN Library provides optimized GPU kernels tailored for generative AI, supporting various tensor shapes, datatypes, quantization, MoE, and LoRA adapters. Friendli TCache intelligently caches frequently used computational results, reducing GPU workload. Additionally, speculative decoding is natively supported, accelerating inference by predicting future tokens in parallel while generating the current token, ensuring identical outputs in less time.

Use cases span from real-time chatbots and content generation to complex AI-driven applications requiring low latency and high throughput. FriendliAI is ideal for enterprises seeking to deploy LLMs at scale without prohibitive GPU costs. Technical details include compatibility with popular frameworks and models, making integration seamless. The engine is available through a pay-per-token pricing model on OpenRouter, offering flexibility for varying workloads. With its focus on performance and cost savings, Friendli Inference empowers businesses to leverage generative AI efficiently.

Use Cases