SemiAnalysis
AI infrastructure and semiconductor research, data models, tools and consulting.
Available information varies by company and source.
Profile record updated:
Company facts
- Official name
- SemiAnalysis
- Entity type
- COMPANY
- Founded
- 2020
- Headquarters
- United States
- Company size
- 50–200
- Market role
- Publisher & Media Owner
- Official website
- semianalysis.com
What SemiAnalysis does
SemiAnalysis converts specialist semiconductor and AI infrastructure research into recurring digital subscriptions and higher-value institutional products. Its analysts produce proprietary market datasets, bottom-up models, benchmarking tools and technical research which customers use for investment, procurement, capacity-planning, engineering and strategic decisions. Institutional customers buy direct access to configured research, models and data products, while organisations with specific requirements engage the firm for advisory, bespoke projects or hourly consulting.
Category differentiation
SemiAnalysis is an independent semiconductor and AI infrastructure research, data and consulting business. It is not a semiconductor manufacturer, cloud provider, chip designer or foundation-model developer.
Strategic context
AI-supported assessment from the existing company research; distinguish interpretation from sourced facts.
SemiAnalysis is a United States-based independent research and analysis firm covering the semiconductor and AI supply chain. It publishes a subscription research publication and sells institutional research, data models, APIs and analytical tools covering accelerators, HBM, cloud GPU economics, data-centre capacity, networking, wafer fabrication and energy demand. It also provides retained advisory, bespoke project work and hourly consulting. Its paying customers include institutional investors, semiconductor manufacturers and equipment vendors, cloud and AI infrastructure providers, data-centre operators, utilities, engineering teams and technology executives. The company generates revenue through recurring individual and institutional subscriptions, separately contracted institutional models and data products, and professional-services engagements.
Company news briefing
Briefing updated:
SemiAnalysis has expanded its market intelligence and benchmarking scope through the acquisition of Citrini Research alongside leadership appointments, while continuing to publish detailed technical assessments of AI infrastructure. Recent firm analyses cover verified agentic inference performance for NVIDIA's Rubin NVL72 platform, Google's TPUv7 Ironwood benchmarks, model parameter offloading architectures, and a parcel-level examination of US datacenter moratoriums indicating minimal overall capacity disruption.
Business model & monetisation
The company monetises recurring access to digital research through individual and institutional subscriptions. It separately sells institutional research configurations, recurring industry trackers, proprietary Excel/PDF models, APIs and research tools through direct commercial engagement. It also earns service fees from retained advisory mandates, bespoke research projects and hourly consulting; public product pages do not disclose standard institutional prices.
- Individual and institutional research subscriptions
- Content Subscription
- Institutional models and recurring data products
- Software Subscription
- APIs and analytical tools
- Pay-per-Use
- Retained advisory, bespoke research and hourly consulting
- Service Fee
Products & capabilities
No products with linked sources are available in this view.
Products & market categories
Media Channel
Recent recorded signals
Dates refer to the source publication. Older entries are historical context, not evidence of a new event.
SemiAnalysis Tests Engram Offloading to DRAM and SSD
AI Infrastructure · Recorded impact score: 4/5
SemiAnalysis analyzes the Engram architecture, a model design that extends standard token embeddings with learned multi-token lookups, allowing for efficient parameter offloading to DRAM or SSD. This reduces HBM capacity requirements for models like DeepSeek-V4.1-Flash. Their experiments show that offloading Engram tables to DRAM can improve performance per dollar, while SSD offloading is currently not economically viable due to overhead. The article also benchmarks inference performance across NVIDIA and AMD GPUs, highlighting NVIDIA's CUDA moat and AMD's slower software support. The analysis includes findings on model behavior, such as gate scan results and ablation studies, and discusses the implications for HBM demand and model architecture innovation.
- SemiAnalysis tested offloading Engram tables to DRAM and SSD for DeepSeek-V4.1-Flash.
- Offloading to DRAM improved performance per dollar, reducing needed HBM capacity.
SemiAnalysis Maps 300 US Datacenter Moratoriums, Finds Minimal Impact
Infrastructure · Recorded impact score: 4/5
SemiAnalysis published a detailed analysis of datacenter moratoriums in the US, arguing that the widespread narrative that these restrictions are killing the datacenter buildout is inaccurate. The analysis covers over 300 local moratoriums and four state-level actions (New York, Texas, Pennsylvania, Oregon). Using a project-by-project, parcel-level analysis of over 6,000 datacenters, the firm found that only approximately 1,525 MW of planned capacity is genuinely delayed by local moratoriums, representing 7.6% of the capacity sitting inside restricted boundaries. The firm's Datacenter Industry Model forecasts +38 GW of new US datacenter IT capacity in 2027, more than double 2026. The report also discusses public sentiment, finding that 46% of Americans view datacenters unfavorably, and examines the political dynamics driving moratoriums.
- SemiAnalysis maps over 300 local datacenter moratoriums and bans across the US, plus four state-level actions (New York, Texas, Pennsylvania, Oregon).
- Only 1,525 MW of planned capacity is directly delayed by local moratoriums, which is 7.6% of the ~20 GW exposed.
Rubin NVL72 Agentic Inference: 67x Better Performance per Dollar
Infrastructure · Recorded impact score: 5/5
SemiAnalysis reports first verified agentic inference results for NVIDIA's Rubin NVL72 platform using their AgentX benchmark. Even on early pre-release software, Rubin delivers up to 67x better performance per dollar of TCO compared to GB300 in certain configurations, and significantly higher throughput per MW. The analysis projects Rubin can generate over 2x more profit per gigawatt than Blackwell, with revenue and profit advantages of 39% and 42% respectively at a fixed power budget. Dynamic power shifting (DSX MaxLPS) allows more GPUs per datacenter footprint. The article highlights Rubin's superiority over H200 and MI355X, with recommendations for inference providers to adopt Rubin for cost-efficient token generation.
- Rubin NVL72 achieves up to 67x the throughput per TCO of GB300 in specific scenarios.
- Rubin delivers up to 7x better token throughput per MW than Blackwell in real-world tests.
Most Neoclouds Fail at Security
Infrastructure · Recorded impact score: 3/5
SemiAnalysis published findings from its ClusterMAX 3.0 security testing (April–July 2026), reporting widespread misconfigurations, out-of-date software, and multiple cross-tenant vulnerabilities across neocloud providers. Tests on 25 providers and 32 clusters uncovered issues including container escapes (e.g., NVIDIA Container Toolkit CVE-2025-23266), Prometheus/Grafana misconfigurations exposing tenant telemetry, incorrect InfiniBand partition and key settings, and BlueField DPU management plane exposure (RShim). The report links these operational failures to real supply-chain risk for major customers (banks, telcos, AI labs, and a national intelligence agency). SemiAnalysis offers a ClusterMAX CLI audit tool, urges enrollment in vendor embargo programs, and recommends systemic patching and stronger isolation practices.
- SemiAnalysis ran ClusterMAX 3.0 security testing across 25 providers and 32 clusters between April and July 2026.
- ClusterMAX provides a CLI (pip install clustermax) and a publicly hosted minimum-versions feed used for automated audits (clustermax.ai).
OpenAI’s Jalapeño Chip Shows Leading Inference Efficiency
Large Language Models (LLM) & AI · Recorded impact score: 4/5
OpenAI revealed Jalapeño — its first custom inference ASIC and rack-scale platform co-developed with Broadcom and Celestica and shown at Hot Chips — with A0 engineering samples taped out Nov 2025. The compute die uses TSMC N3P, MXFP numeric formats and HBM4 (15.4 TB/s per package); package TDP is ~700 W with sustained test power ≲ ~550 W. The rack design keeps model state (KV cache) local, simplifies on-node fabric and can scale to 2,048 XPUs. OpenAI and SemiAnalysis/InferenceX benchmarks report substantial performance-per-watt and latency gains (and partial advantages vs Nvidia GB300), but results are not independently verified and did not include Nvidia Vera Rubin. OpenAI targets small-volume deployment end‑2026 and broader ramp in 2027, pursuing a multi‑vendor production strategy while production economics, yield and fleet reliability remain unproven.
- Announced at Hot Chips; co-developed with Broadcom and Celestica; A0 engineering samples taped out Nov 2025. Compute die: TSMC N3P, MXFP numeric formats, HBM4 (15.4 TB/s per package); package TDP ~700 W with sustained test power ≲ ~550 W.
- Rack-scale architecture keeps KV cache local, simplifies on-node fabric and can scale to 2,048 XPUs.
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Questions about SemiAnalysis
What is SemiAnalysis?
SemiAnalysis is an independent research firm providing semiconductor and AI infrastructure publications, institutional models, data products, tools and consulting.
Who uses SemiAnalysis?
Its customers include institutional investors, semiconductor companies, cloud and AI infrastructure providers, data-centre operators, utilities, engineers and technology executives.
How does SemiAnalysis make money?
It earns recurring subscription revenue, sells institutional research and data products, and charges for advisory, bespoke projects and hourly consulting.
Sources & coverage
This profile uses public, official and technically observable information. Missing information does not prove that a product or relationship does not exist. The list below does not imply that every profile statement has been verified.
14 publicly documented primary sources and citations linked across the market graph.
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