Question 1

What is the Open-Prem Inflection Point?

Accepted Answer

The Open-Prem Inflection Point is a framework created by David Borish that identifies the convergence of three factors that fundamentally alter enterprise AI deployment economics: open-source LLMs matching or approaching proprietary model capabilities, decreasing hardware cost barriers to on-premises deployment, and the privacy, security, and compliance advantages of keeping AI infrastructure in-house.

Question 2

Who created the Open-Prem Inflection Point framework?

Accepted Answer

David Borish, Enterprise AI Strategist at Trace3 (an Apollo Management company), created the Open-Prem Inflection Point framework. He delivers the Open-Prem Strategy Accelerator workshop in partnership with IBM for enterprise clients.

Question 3

What does open-prem mean?

Accepted Answer

Open-prem refers to the deployment of open-source AI models on an organization's own on-premises infrastructure, as opposed to using proprietary AI models through cloud-based APIs. The term was coined by David Borish to describe the convergence of open-source model maturity and on-premises hardware economics that creates a strategic alternative to cloud-dependent AI.

Question 4

What open-source models does the original paper analyze?

Accepted Answer

The original Open-Prem Inflection Point paper analyzes DeepSeek-V3 (671B parameters, Mixture-of-Experts architecture), Mistral Large Instruct 2411 (123B parameters), Qwen 2.5 72B, and Llama 3.3 70B. Benchmark tests showed these models achieving 85-90% of proprietary model capabilities at a fraction of the cost.

Question 5

How much can organizations save with on-premises AI deployment?

Accepted Answer

According to the Open-Prem Inflection Point paper, organizations can save $100,000 to over $1 million on data privacy and security costs alone by moving to on-premises solutions. Cloud per-token costs range from $0.50-$15.00 per million tokens, while on-premises costs run $0.10-$0.30 per million tokens. For organizations processing 500 million tokens monthly, the breakeven point typically occurs within 12-18 months.

Question 6

What industries benefit most from on-premises AI deployment?

Accepted Answer

The paper identifies healthcare, financial services, and manufacturing as industries that benefit most from on-premises AI. Healthcare organizations processing protected health information, financial institutions managing customer data, and manufacturing companies protecting intellectual property all achieve risk reduction through on-premises control. Case studies in the paper cover implementations across all three sectors.

Question 7

What hardware is needed for on-premises AI deployment?

Accepted Answer

The paper outlines three deployment scales: small-scale (1-10 users) requiring $15,000-$25,000 in hardware using Llama 3.3, medium-scale (10-50 users) requiring $75,000-$150,000 using Qwen 2.5 or Llama 3.3, and large-scale (50+ users) requiring $200,000-$400,000 using DeepSeek-V3. Quantization techniques reduce VRAM requirements by 75-80% with minimal performance impact.

Question 8

What is the difference between cloud AI and open-prem AI deployment?

Accepted Answer

Cloud AI uses proprietary models accessed through paid APIs with per-token pricing, limited customization, and data processed on external servers. Open-prem deploys open-source models on an organization's own hardware, providing predictable costs, complete data sovereignty, full model customization and fine-tuning capability, and simplified regulatory compliance. The Open-Prem Inflection Point paper provides a detailed cost comparison across seven factors.

DAVID BORISH

THE OPEN-PREM
INFLECTION POINT

When Open-Source LLMs and Hardware Economics Converge

KEY FINDINGS

ABOUT THE AUTHOR

Back to top

THE OPEN-PREM INFLECTION POINT

When Open-Source LLMs and Hardware Economics Converge

KEY FINDINGS

ABOUT THE AUTHOR

​

Back to top

THE OPEN-PREM
INFLECTION POINT