Modern LLMs use reward models—trained to reflect human preferences—to align their behavior through RLHF. While effective, this approach faces challenges like reward hacking and Goodhart's law. New research offers solutions such as verifiable feedback, constrained optimization, and self-critiquing models to improve alignment and reliability in complex tasks.
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Transformers have powered today’s AI revolution—but limitations around speed, memory, and scalability are becoming clear. This article explores three promising alternatives: diffusion-based LLMs that generate text in parallel for faster, more controllable outputs; Mamba’s state space models, which scale to million-token contexts without quadratic costs; and Titans, a memory-augmented architecture that can learn new information at inference time. Each approach tackles core challenges in latency, context handling, and long-term reasoning—opening new opportunities for businesses to reduce compute costs and deploy smarter, more adaptable AI systems.
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As AI evolves toward reasoning models and near-AGI, enterprises need secure, scalable, and compliant infrastructure. Apolo offers an on-prem, future-ready AI stack—built with data centers—that supports model deployment, fine-tuning, and inference at scale. Designed for privacy, agility, and rapid AI growth, Apolo empowers organizations to stay in control as the AI revolution accelerates.
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This publication is not intended to be a comprehensive review of all GPUaaS market offerings and has not been tailored for any particular use case. Readers should carefully consider their own circumstances and objectives when making purchasing decisions. All information herein is provided "AS IS" and without any warranty, express, implied or otherwise, regarding its accuracy or performance, and Apolo Cloud Inc., a Delaware corporation (“Apolo”), has not independently verified the claims of the market participants referenced herein. Apolo disclaims all liability for any reliance upon this publication and does not undertake to update or revise this publication, including to reflect subsequent developments.
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AI is transforming data centers, enabling businesses across industries to drive real revenue through faster, smarter infrastructure. Apolo’s multi-tenant MLOps platform supports these advancements, allowing companies to unlock the full potential of AI for tangible business outcomes.
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AI and ML are transforming network modernization by automating testing, validation, and optimization, ensuring networks remain agile and future-proof. In this post, Bill Kleyman, our CEO, explains how AI-driven tools are revolutionizing network performance and making infrastructure smarter, faster, and more efficient.
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As data centers face rising energy demands, nuclear power is emerging as a sustainable and reliable solution. In this post, Bill Kleyman, our CEO, explores how nuclear energy could revolutionize data center efficiency and reduce their carbon footprint.
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As data centers consume significant energy, sustainability is crucial. This blog highlights trends like advanced cooling, renewable energy, and AI-driven optimization to enhance data center efficiency. Discover how Apolo can help make your data center more sustainable and energy-efficient.
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Data centers are seeing a surge in rack density due to the growing demand for AI and high-performance computing. But even with density doubling, traditional cooling and power management systems are struggling to keep up. Learn how innovations like liquid cooling are driving the future of data center efficiency.
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