Arm vs SemiAnalysis

Arm CEO Confident $2B AI CPU Revenue Goal Achievable

Arm Holdings CEO Rene Haas told CNBC's Jim Cramer on September 16, 2026, that the company is increasingly confident it can meet Wall Street's $2 billion revenue target for its new in-house data center CPU, the AGI CPU. The demand for the chip has been strong, but investors have focused on Arm's ability to secure sufficient manufacturing capacity to convert that demand into revenue. Haas noted that confidence in achieving the $2 billion figure has grown from May to July and further strengthened by September. The AGI CPU marks a significant expansion of Arm's business model, moving from licensing chip designs to selling complete chips. Arm first disclosed $2 billion in demand in May, double its initial $1 billion outlook, but maintained a conservative official outlook until supply confidence improved. Shares have declined about 45% from their June high after a parabolic run earlier in the year.

• Arm Holdings CEO Rene Haas expressed increased confidence in meeting a $2 billion revenue goal for its AGI CPU data center chip.
• The $2 billion target is double the $1 billion demand Arm initially outlined in March 2026.
• Arm's stock fell 10% after maintaining a $1 billion revenue outlook due to supply concerns in May.

Arm introduces new AI-native compute platform built for agentic AI and mobile graphics

Arm unveils Arm AI Portal, expands AI infrastructure with AGI CPU and Neoverse CSS N4, and brings ecosystem together for physical AI.

Arm introduces new AI-native compute platform built for agentic AI and mobile graphics

Arm announced a new AI-native compute platform for agentic AI and mobile graphics, expanded AI infrastructure with AGI CPU and Neoverse CSS N4, and unveiled the Arm AI Portal to accelerate optimized AI apps.

SemiAnalysis Tests Engram Offloading to DRAM and SSD

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.
• SSD offloading was not economically viable, with DRAM delivering 121 million tokens per dollar versus 52 million for SSD.

SemiAnalysis Maps 300 US Datacenter Moratoriums, Finds Minimal Impact

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.
• SemiAnalysis forecasts +38 GW of US datacenter IT capacity delivered in 2027, more than double 2026.

Rubin NVL72 Agentic Inference: 67x Better Performance per Dollar

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.
• Rubin can generate over 2x more profit per gigawatt than Blackwell.