Google Cloud Managed Luster Overview - Google Cloud Managed Luster is a fully managed service running on top of DDNX Scaler, launched 4 and a half months ago [1][7] - It addresses the need for high throughput and low latency storage in HPC environments to keep GPUs and CPUs efficiently fed with data [4][5] - The service is integrated with Google Cloud services like DCS and GKE, offering easy data import/export from/to Google Cloud Storage [7][8] Performance and Scalability - Google Cloud Managed Luster offers up to 1 TB (Terabyte) per second of throughput with sub-millisecond latency and millions of IOPS [9] - It scales from a starting size of 9 TB (Terabyte) up to 8 PB (Petabyte) [9] - Performance tiers range from 125 MB (Megabyte) per second per TB (Terabyte) to 1,000 MB (Megabyte) per second per TB (Terabyte), catering to different throughput needs [15] Customer Benefits and Use Cases - Customers have experienced significant performance improvements, with one customer, Resemble AI, achieving full GPU saturation and 6x faster performance compared to other storage solutions [10] - Sony Honda Mobility's department, Fila, saw a 3x performance improvement compared to their previous storage solution [17] - Key use cases include KV cache, multimodal training, and checkpointing, all requiring low latency and high throughput [11][13][14] Partnership with DDN - Google partnered with DDN (DataDirect Networks) due to their mature, reliable Exoscaler product with a rich feature set [6] - The partnership aims to provide a fully managed solution, relieving customers of storage management burdens [6]

Summary of Key Points from the Conference Call Industry Overview - The focus is on the **AI infrastructure** build-out, particularly in **Tier 1 data center markets**. The demand for AI is shifting from large super-compute facilities to more accessible metro colocation sites for better connectivity [1][2]. Core Companies Mentioned - **Digital Realty Trust (DLR)**: Positioned to attract customers seeking a balance of large footprint and low latency [2]. - **Equinix (EQIX)**: Best positioned for low latency workloads due to its leading interconnection offerings and market share in carrier hotels [2]. - **Iron Mountain (IRM)**: Smaller portfolio but strategically located in well-connected Tier 1 markets [2]. Key Insights on Latency - **Latency** is critical for AI applications, with inference tasks requiring sub-100 ms round-trip latency. The location of data centers significantly impacts this latency [3][16]. - **Tier 1 markets** are essential for low-latency applications, as they provide geographic proximity to end-users, which is crucial for applications like autonomous driving and high-frequency trading [21][22]. Enterprise Demand Trends - Enterprises are increasingly seeking smaller, low-latency infrastructure in Tier 1 markets rather than large-scale data centers in remote areas. This trend is driven by the need for fast, reliable performance for critical workflows [4][60]. - **AI adoption** is expected to accelerate enterprise demand for colocation services, with companies like DLR and EQIX reporting record leasing activity driven by AI-related demand [5][65]. Financial Projections - Data Center REITs are expected to deliver above-consensus revenue growth, with annual AFFO/share growth projected in the high single to low double digits [5]. - **Price targets** for key stocks are set as follows: DLR at $206.00, EQIX at $1,050.00, and IRM at $120.00 [6]. Market Dynamics - Vacancy rates in Tier 1 markets have dropped to historic lows, with power availability becoming a primary bottleneck for new developments. North America's vacancy rate fell to just **1.6%** in Q3 2025 [35]. - The supply-demand imbalance is enhancing landlords' pricing power, with DLR reporting **+8% cash re-leasing spreads** and IRM seeing **+13.9%** [36]. Competitive Landscape - Operators with existing inventory and rapid delivery capabilities are winning multi-megawatt pre-leases from hyperscalers and AI firms. The competitive advantage is increasingly tied to power access and network density [37]. - New entrants, primarily former Bitcoin mining firms, are pivoting to AI hosting but are building in remote locations, which may not meet the proximity needs for latency-sensitive applications [66][68]. Conclusion - The AI infrastructure landscape is evolving, with a clear bifurcation between large training clusters in remote areas and distributed inference workloads in Tier 1 markets. Companies with strong metro footprints and interconnection capabilities are best positioned to capture this growing demand [23][24].

Hello, and welcome. In this video, we'll guide you through an overview of the AMD Versal Network on Chip, NoC, and discuss key strategies for its performance tuning. We will first start with an introduction to the AMD Versal Network on Chip, NoC, followed by the NoC architecture and terminology. Then we will show you how to access the Versal NoC for your designs.And finally, we will go over important NoC settings needed to achieve a desired bandwidth and latency. Let's get started. The AMD Versal Network on ...

Solana Network Performance - Solana's Alpenglow upgrade is expected in early 2026, targeting 100ms finality [1] - This finality is projected to be 120x faster than Ethereum's 12-second blocks [1] DeFi Implications - Phoenix and Drift can potentially operate order books with latency comparable to centralized exchanges (CEX) [1] - Solana aims to eliminate the need for separate CEX and DEX strategies for market makers [1] - DeFi protocols designed for 400ms latency could experience a 4x speed improvement [1]

Technology Improvement - Solana's read layer achieves 10x lower latency [1] - The system requires 100x fewer RPC calls [1] - The codebase is reduced by 1000x [1]

Solana Data Redesign - Solana 历史/存档数据已被重新设计，旨在解决最大的数据/RPC问题 [1] - 新设计旨在将延迟降低 10 倍，RPC 调用减少 100 倍，代码减少 1000 倍 [1] Technical Improvements - 查询历史数据（getBlock/getTransaction/getSignaturesForAddress）不再直接访问 Google BigTable [1]

User Experience & Performance - Users prioritize the capacity and latency improvements that chains can achieve, leading to better apps and UX [1] - Chains settling to Ethereum can offer performance identical to any L1 but with reduced overhead (consensus) [1] Security & Trust - While chains aim for security parity or improvement over time, this is not the primary concern for users [2] - Existing L2s have a permissioned multisig that can override the bridge contract without notice [2] - Escape hatch is a property of the bridge, not the L2 itself [2] Technical Feasibility - There is no engineering blocker to build a bridge on Solana [2]

Performance Upgrade - Sei Giga 升级将实现低于 400 毫秒的最终确认时间，以及每秒 5 Gigagas 的吞吐量（约 20 万 TPS）[1] - Sei Labs 工程师发现了节点运营商延迟优化方案，可带来 10 到 40 倍的性能提升 [1] Technical Analysis - Sei Labs 在准备 Sei Giga 升级时，使用不同的状态存储后端对节点性能进行了基准测试 [1] - 在比较 RocksDB 和带有 MVCC 的 PebbleDB 用于索引繁重的历史查询时，RocksDB 能够将节点运营商延迟降低 10 到 40 倍 [2]

RT nick | helius.dev (@nick_pennie)ok fine@heliuslabs stats from yesterday- 100k+ peak TPS- 99.999% uptime- no change in latencycombo that with our 24/7 support and there's simply no better optiontldr: put your marketing department to work instead of your engineers https://t.co/x1t6x9z2xY ...

Custom Solutions - A small percentage of hyper latency sensitive users could benefit from a custom solution [1] - Dedicated node for YS gRPC streaming from Helius can be pointed to shreds for quicker data [1] Performance Improvement - Shaving off approximately 0.5 milliseconds in data processing time is possible [1] Risk Consideration - The custom solution is not fault tolerant, unlike Laserstream [1]