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What NVIDIA’s role in open source AI means for the economics of compute.
Independent AI infrastructure research
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BEP connects engineering, market data, and investment research to explain the businesses being built around AI.
Selected research
What NVIDIA’s role in open source AI means for the economics of compute.
Ben Pouladian and Rick Xie examine the constraints behind AI memory demand.
A review of BEP’s published NVIDIA work ahead of the company’s earnings.
An observation from The Stack
LLMflation follows a weighted basket of model output prices against GPT-4’s March 2023 launch price. It’s one way our research moves from a narrative to a number.
Explore the market dashboard The full dashboard is included with a paid subscription.LLMflation
As of 2026-09-10
A price-basket comparison, not a measure of equivalent model quality or task success. Weights: OpenAI 30%, Anthropic 25%, Google 20%, DeepSeek 15%, open-weight models 10%. The launch reference is $60 per million output tokens.
Put the research to work
GPU and token prices, infrastructure costs, and the margins behind AI.
Track how tight compute markets are, which way they’re moving, and what drives the change.
Interactive supply chains, AI task economics, and the research call sheet.
People behind the work
Our work brings together the BEP team and specialist research collaborators. The memory series is one example: engineering and standards analysis paired with the economics of the business.
Founder · BEP Research
Electrical engineer, operator, and investor. Ben leads BEP Research and the investment framing of our collaborative memory series.
About Ben ↗Research collaborator · memory systems
A memory-systems researcher and co-author of our memory series. Rick leads its engineering and standards analysis, from DRAM to SSDs.
Read the collaboration ↗BEP Research membership
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