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NVIDIA
AI Datacenters
Inferencex
Vera Rubin NVL72
Electricity Prices
Memory
AMD

Everything semiconductors and AI

Covering the spectrum

PublishesWeeklyEpisodes5Foundeda month ago
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Artwork for SemiAnalysis Weekly

Latest Episodes

This episode explores the impact of AI on memory costs, market dynamics, and economic measurement. Our experts discuss how AI is transforming industries, the challenges of measuring economic value, and the future of AI adoption across sectors. This w... more

Join Jordan Nanos, Doug O’Laughlin, and special guest Jeremie Eliahou Ontiveros for an in-depth analysis of the intersection between AI infrastructure and energy markets. Jeremie, Head of Datacenter & Energy Infrastructure Research, shares expert ins... more

Article (latest): newsletter.semianalysis.com/p...

00:00 Introduction to Vera Rubin and Extreme Co-Design

02:30 Innovations in GPU Architecture: From Blackwell to Rubin

05:38 Memory Bandwidth and HBM4: A New Era

08:26 NVLink 6 and Int... more

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Recent Guests

Jeremie Eliahou Ontiveros
Lead data-center energy modeler for SemiAnalysis
SemiAnalysis
Episode: Impact of AI Datacenters (Jeremie Eliahou Ontiveros, Jordan Nanos, Doug O'Laughlin)
Cam Quilici
Member of InferenceX team
InferenceX
Episode: Feb 19, 2026 - InferenceX (Cam Quilici, Bryan Shan, Doug O'Laughlin, Jordan Nanos)
Bryan Shan
Member of InferenceX team
InferenceX
Episode: Feb 19, 2026 - InferenceX (Cam Quilici, Bryan Shan, Doug O'Laughlin, Jordan Nanos)

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Talking Points

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Feb 19, 2026 - InferenceX (Cam Quilici, Bryan Shan, Doug O'Laughlin, Jordan Nanos)
Q: How do these optimizations translate to real-world costs and deployment?
MTP and related optimizations significantly reduce cost per million tokens and increase tokens per second per user, enabling cheaper operation and potential end-user savings, while still allowing benchmarking to reflect real-world configurations, such as multi-node setups and mixed hardware fleets.
Feb 19, 2026 - InferenceX (Cam Quilici, Bryan Shan, Doug O'Laughlin, Jordan Nanos)
Q: Why is there a 2.0 release and what are the major changes from 1.0 to 2.0 moving to InferenceX?
2.0 expands beyond single-node benchmarks to include disaggregated pre-fill, wide expert parallelism, speculative decoding, and broader hardware support (GB200/GB300) with multi-node testing, aiming for a more nuanced, production-like view of frontier inference and improved throughput and cost metrics.

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