Growing the Cloudflare AI team with talent from Ensemble AI (opens in new tab)
Cloudflare is bringing key members of Ensemble AI onto its team to improve AI infrastructure and inference efficiency. Ensemble’s work on model compression, structured neural architectures, and parameter-efficient fine-tuning complements Cloudflare’s Workers AI platform. The combined effort aims to make powerful AI models faster, cheaper, and easier to deploy globally. ## Incorporating Ensemble AI’s Expertise - Ensemble AI has focused on reducing the memory, compute, and deployment costs of large language and multimodal models. - Its NdLinear technology replaces standard transformer linear layers while preserving multidimensional structure such as attention heads, channels, and spatial dimensions. - NdLinear-LoRA reduces the number of trainable parameters needed to fine-tune large models. - These techniques complement quantization and vector quantization to improve model efficiency without significantly sacrificing quality. ## Improving AI Inference Economics - Cloudflare Workers AI provides serverless GPU-powered inference across Cloudflare’s global network. - Lower model size, memory usage, and compute requirements can improve throughput, GPU utilization, and overall inference costs. - These improvements are increasingly important for agents, multimodal applications, personalization, fine-tuning, retrieval, and reinforcement learning. - The Ensemble team will contribute to Cloudflare’s existing work, including the Infire inference engine, Unweight tensor compression, and systems for running very large language models. ## Supporting Next-Generation Workloads - Developers increasingly need AI infrastructure that is reliable, affordable, globally distributed, and close to end users—not merely access to models. - Cloudflare’s network, serverless platform, and Workers AI provide a foundation for deploying AI with less operational complexity. - Combining Cloudflare’s infrastructure with Ensemble’s efficient model architectures should enable lower-cost, higher-performance AI deployments at scale. Cloudflare’s stated goal is to make advanced AI workloads more accessible by improving the economics and efficiency of inference across its platform.