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Open, Private, Cost-Effective AI: Arcee.ai Foundation Models on Intel Xeon
Davis Stone, Head of Growth and Partnerships at Arcee.ai, Julien Simon, Chief Evangelist at Arcee.ai & Shannon Warner is the Director, ISV Global Partner Scale at Intel
Aug 12 2025 | 33 mins
- Details
Enterprises today want powerful AI capabilities—but face challenges with costly infrastructure, vendor lock-in, and compliance risks. Large language models (LLMs) often require expensive GPUs and cloud services, raising concerns about data privacy, security, and control.
Join Intel and Arcee AI to explore Arcee Foundation Models (AFM)—a family of small, efficient language models delivering state-of-the-art AI quality on Intel Xeon CPUs. AFM provides comparable performance to much larger models while significantly lowering hosting costs and infrastructure complexity.
Designed for enterprises that demand cost-efficiency, privacy, security, and regulatory compliance, AFM offers full transparency with open weights and architecture, eliminating vendor lock-in. This allows organizations to deploy AI on-premises, in the cloud, or in hybrid environments, maintaining full ownership of their infrastructure and data.
In this session, you’ll learn how AFM helps decision makers to:
- Achieve state-of-the-art AI performance optimized for Intel Xeon CPUs
- Reduce the total cost of ownership by avoiding expensive GPU requirements
- Maintain data privacy, compliance, and sovereignty for regulated environments
- Leverage an open, transparent foundation model for maximum control
- Accelerate deployment with AFM’s domain adaptation for industry-specific solutions
Whether you’re leading digital innovation, IT strategy, or compliance, this webinar will provide actionable insights on deploying secure, high-quality, and affordable AI at scale.
Discover how Arcee Foundation Models on Intel Xeon empower your enterprise to adopt next-generation AI—small in size, big on impact, fully under your control.
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Shannon Warner Bio PDF document
56 KB
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Davis Stone Bio PDF document
72 KB
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Julien Simon Bio PDF document
65 KB
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