Question 1

What changed in the Open-Prem Inflection Point V2?

Accepted Answer

The V2 paper, published October 2025, demonstrates that the Open-Prem thesis has been validated through widespread enterprise adoption. Open-source models now deliver competitive performance at significantly lower costs. The paper incorporates new models including Qwen3-Omni (the first natively end-to-end omni-modal AI model), Llama 4 with Mixture-of-Experts architecture, IBM Granite 3.2/3.3 with toggleable reasoning, and DeepSeek V3.1. It also adds named enterprise case studies and updated hardware economics.

Question 2

What is the 2 million token threshold in the V2 paper?

Accepted Answer

The V2 paper establishes that organizations processing over 2 million tokens daily now achieve compelling return on investment with typical payback periods of 6-12 months for on-premises deployment versus continued cloud API usage. Organizations under 500,000 tokens daily should continue using cloud APIs, while those between 500,000 and 2 million benefit from hybrid approaches.

Question 3

What enterprise case studies does the V2 paper include?

Accepted Answer

The V2 paper includes named case studies: Jabil achieved 67-83% reduction in deployment times across 100+ global sites. ANZ Bank built an engineering efficiency platform on Llama models. Wells Fargo deployed multiple internal LLM applications. Cedars Sinai achieved 95%+ accuracy in brain tumor classification using fine-tuned Llama 3. Evolven achieved a 25x cost reduction transitioning from cloud to open-premises deployment. Klarna reduced resolution times from 11 minutes to under 2 minutes.

Question 4

How close are open-source models to proprietary models in the V2 analysis?

Accepted Answer

The V2 paper shows the performance gap has effectively closed. Qwen3-Omni achieved 88.7% on MMLU versus Google Gemini Ultra at 90.0%, a margin of just 1.3%. DeepSeek-V3 leads pure language benchmarks with 88.5% MMLU and 75.9% MMLU-Pro. The paper states that open-source models provide superior value for most use cases when considering total cost of ownership, customization, and data sovereignty.

Question 5

What new hardware options does the V2 paper cover?

Accepted Answer

The V2 paper adds analysis of the RTX 5090 ($2,000-$3,800) achieving 5,841 tokens/second and 2.6x faster than A100 performance. Dual RTX 5090 configurations match H100 capabilities for 70B models at 25% of the cost. AMD MI300X offers 192GB memory at 25-30% lower cost than H100. Intel Gaudi 3 provides 33-50% cost reduction with standard Ethernet networking.

Question 6

What security data does the V2 paper present?

Accepted Answer

The V2 paper reports US data breach costs reached $10.22 million annually in 2025, shadow AI breaches cost an additional $670,000 per incident, 20% of breaches involve shadow AI, 63% of organizations lack AI governance policies, and 83% of companies experienced cloud security breaches in the past 18 months. EU AI Act fines can reach up to 35 million euros or 7% of global turnover.

Question 7

How does the V2 paper relate to the IBM partnership?

Accepted Answer

David Borish created the Open-Prem Inflection Point framework and delivers the Open-Prem Strategy Accelerator workshop in partnership with IBM for enterprise clients. The V2 paper includes analysis of IBM Granite 3.2 and 3.3 models, which offer enterprise-focused features including toggleable reasoning capabilities that allow switching between conversational and deep reasoning modes.

Question 8

What is the Evolven case study in the V2 paper?

Accepted Answer

Evolven, an enterprise software company with 14 years of AI/ML experience, transitioned from cloud-based AI to open-premises deployment and achieved a 25x cost reduction. Their API costs were projected to increase by 2-3 orders of magnitude. After implementing vLLM with Qwen models, they process approximately 1 million tokens per user per day with 100% consistent model responses, complete data sovereignty, and predictable versioning.

DAVID BORISH

THE OPEN-PREM
INFLECTION POINT V2

When Open-Source LLMs and Hardware Economics Converge

WHAT CHANGED IN V2

ABOUT THE AUTHOR

FREQUENTLY ASKED QUESTIONS

What changed in the Open-Prem Inflection Point V2?

What is the 2 million token threshold in the V2 paper?

What enterprise case studies does the V2 paper include?

How close are open-source models to proprietary models in the V2 analysis?

What new hardware options does the V2 paper cover?

What security data does the V2 paper present?

How does the V2 paper relate to the IBM partnership?

What is the Evolven case study in the V2 paper?

Back to top

THE OPEN-PREM INFLECTION POINT V2

When Open-Source LLMs and Hardware Economics Converge

WHAT CHANGED IN V2

ABOUT THE AUTHOR

​

FREQUENTLY ASKED QUESTIONS

What changed in the Open-Prem Inflection Point V2?

What is the 2 million token threshold in the V2 paper?

What enterprise case studies does the V2 paper include?

How close are open-source models to proprietary models in the V2 analysis?

What new hardware options does the V2 paper cover?

What security data does the V2 paper present?

How does the V2 paper relate to the IBM partnership?

What is the Evolven case study in the V2 paper?

Back to top

THE OPEN-PREM
INFLECTION POINT V2