Clarifai’s new reasoning engine makes AI models faster and less expensive (techcrunch.com)

Clarifai announced a new reasoning engine for inference workloads that the company says can run AI models up to twice as fast and cost about 40% less. Built to be model- and cloud-agnostic, the engine layers a suite of low-level and algorithmic optimizations — from tuned CUDA kernels to advanced speculative decoding — to squeeze more throughput out of the same GPU hardware. The product specifically targets inference for multi-step, agentic and reasoning models, which multiply compute demands by executing many intermediate steps or calls in response to a single user prompt. The significance for the AI/ML community is practical: as demand for large-scale, multi-step models surges, software-side efficiency can blunt the need for massive new data-center buildouts and cut immediate GPU spend. Clarifai, which began as a computer-vision provider and has been pivoting into compute orchestration, frames this engine as the first of its offerings tailored to agentic workflows. If the claimed gains hold up in real-world deployments, the engine could meaningfully reduce operational costs and latency for production inference, complementing hardware investments and ongoing algorithmic advances to make large-model deployment more sustainable.