[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-high-bandwidth-flash-a-new-memory-for-ai-data-centers-and-edge-computing-1":3,"$fjnKX72BiLw-CiZZ5MQCs0a0C_bANyAZUVd6PqPzBgJg":30,"$f-j9YGNBwRSSL7SH4JzUpRZrBt_Eb-enBkRymgC2HvJg":166,"$fR4DoeVRVekscO3pYCsJIt6KjRPIynjtNlytHFj6IDMg":207,"$fx0Dz-WUAXgaGJDFKp5JoFihuourpw4oC0uNO6EDK9Y8":213,"$flyRSpjhyVE29pqjd-DzWtoRwNGPXrTq6WJDGSvhczcc":245},{"data":4},{"id":5,"title":6,"slug":7,"excerpt":8,"content":9,"imageUrl":10,"socialImageUrl":11,"publishedAt":12,"author":13,"category":14,"likes":15,"summary":16,"captionLinkedin":17,"captionTwitter":18,"captionInstagram":19,"seo":20,"prevPost":22,"nextPost":26},1471,"High Bandwidth Flash: A New Memory for AI Data Centers and Edge Computing","high-bandwidth-flash-a-new-memory-for-ai-data-centers-and-edge-computing-1","Discover how High Bandwidth Flash (HBF) is set to disrupt AI data centers by overcoming DRAM limitations to better support massive AI inference workloads.","\u003Cp style=\"text-align: left;\">\u003Cimg style=\"float: left;\" src=\"https:\u002F\u002Fmedia.magicwandmedia.in\u002Fposts\u002FJune2026\u002Fnews\u002Fhigh-bandwidth-flash-a-new-memory-for-ai-data-centers-and-edge-computing-content-1.jpeg\" alt=\"\" width=\"249\" height=\"249\">Artificial intelligence is on a relentless march across the computing landscape. While about one in seven data centers today is equipped to host AI workloads, that figure is expected to approach 70 percent by 2030. According to Alper Ilkbahar, CTO, Sandisk, AI is migrating from hyperscale to enterprise data centers and out to the network perimeter, where edge AI applications are projected to generate nearly US$ 66.5 billion by the end of the decade. The fuel for the new computing era is data: staggeringly large volumes that must be fed at high speed to demanding and rapidly scaling AI computing infrastructure.\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">These vast content repositories are overwhelming conventional storage structures and bring an inherent architectural weakness into sharp relief. Data center memory (DRAM and specialized high bandwidth memory known as HBM) is increasingly struggling to keep pace with the growing demands of large AI models in terms of density, storage capacity and scalability. At the same time, hyperscale computing manufacturers are contending with rising DRAM and HBM production costs, design complexity and energy consumption. The challenge is even more daunting in enterprise data centers and edge AI applications where a proportionately smaller physical footprint renders them ill-equipped to absorb rising memory costs and power use.\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">And there is another pressing issue introduced by AI inference, which is now the dominant AI workload and has different data management requirements from those of AI training. Inference stores large – and growing – AI models, and HBM and DRAM-based memory have shown they lack the capacity and  cost scalability to keep up with these new demands. Given these distinctly different memory characteristics, an opportunity exists for a memory technology optimized specifically for AI inference.\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">\u003Cstrong>Why DRAM and HBM Underserve AI Inference Workloads\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">To understand why DRAM and HBM alone are suboptimal for long-term AI deployment, consider the following drawbacks. These began as small fissures, but if uncorrected, will expand over time to undermine the foundation of next-generation AI-centric storage.\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">\u003Cstrong>\u003Cem>Density Penalties\u003C\u002Fem>\u003C\u002Fstrong>\u003Cem>:\u003C\u002Fem> DRAM capacity scaling has stalled while the need for higher capacity to address AI inference is growing.\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">\u003Cstrong>\u003Cem>Mismatched for AI Inference\u003C\u002Fem>\u003C\u002Fstrong>\u003Cem>:\u003C\u002Fem> The advantages of DRAM’s low latency and random access characteristics are not relevant for AI inference, where access patterns are deterministic and more latency tolerant thanks to techniques like data prefetching.\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">\u003Cstrong>Attributes of an Optimized AI Inference Memory Architecture\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">These fault lines run beneath a US$ 120 billion DRAM industry that is eager to retain its hold on the data center, given that spending by hyperscale providers on AI infrastructure could reach US$ 6.7 trillion by the end of the decade.\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">What if it’s time to make a clean break and design a new memory from the ground up that meets the needs of the application, rather than the other way around? An AI-tuned storage-class memory would have the following attributes: larger and scalable memory capacity provisioned for inference workloads; higher memory density (GB\u002Fmm²); high bandwidth to meet the requirements of AI inference; lower system-level power consumption; and cost-effective metrics ($\u002FTB).\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">\u003Cstrong>High Bandwidth Flash Takes Aim at the AI Data Center\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">High Bandwidth Flash (HBF™) is a disruptive new memory architecture, purpose-built to drive the next generation of AI computing. HBF meets the capacity, energy, throughput, and scalability requirements of advanced computing and data-intensive applications. Compared to HBM, HBF provides higher capacity and memory density with comparable bandwidth that better aligns with AI inference trends. As a persistent storage medium, HBF also retains data when power is lost and is thermally stable, supporting high operating temperatures.\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">To realize these advantages, HBF leverages Sandisk’s BiCS design and manufacturing technology and die architecture, which effectively redesigns NAND flash by optimizing for high bandwidth and inference memory characteristics. The use of BiCS CMOS bonded array (CBA) wafer technology further enhances energy efficiency and bandwidth.\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">\u003Cstrong>HBF Reimagines NAND Flash for AI Applications\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">Compared to conventional NAND flash, HBF’s use of parallelism, advanced logic scaling, and custom stacking techniques helps deliver lower latency and significantly higher read bandwidth, enabling large language models to stream data at near-DRAM speeds.\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">HBF also includes support for large KV caches to efficiently handle long, complex user prompts, and customer- and domain-specific data that helps improve AI inference accuracy.\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">\u003Cstrong>Extending Memory-Centric AI to the Enterprise and Network Edge\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">Because HBM is not generally available for use in edge and mobile environments due to density, cost and power penalties, the value of larger memory capacity for handling more complex AI inference problems is realized with HBF. This opens the door for edge devices like smartphones that are capable of making real-time decisions to manage a variety of sophisticated tasks. Thanks to its persistent memory, HBF supports the ability to seamlessly retrieve old context from previous queries to solve new problems.\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">The advantages of HBF extend to enterprise-level computing, where the user base is much smaller than hyperscale data centers and large GPU clusters supported by HBM are too costly. By adopting HBF-enabled accelerators, smaller enterprises can potentially fine-tune large, pre-trained models for domain-specific uses.\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">\u003Cstrong>Optimized Memory Removes Obstacles to AI Computing Growth\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">All around us, data centers and edge AI devices operate autonomously, supporting tasks that range from tonight’s dinner recipe to groundbreaking scientific discoveries. Routine tasks like website hosting and enterprise data management are giving way to intelligent workloads that generate actionable insights using machine learning, deep learning, and data analytics.\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">It’s time to reconsider how data center and edge memory are provisioned to manage large-scale inference models that make predictions and generate outputs. Compared to HBM, HBF has a clear capacity advantage while delivering the high throughput required by AI inference applications. As a scalable new system memory technology, HBF helps reduce performance bottlenecks and accelerates time-to-insight for AI applications in modern data centers and edge networks alike.\u003C\u002Fp>\n\u003Cp style=\"text-align: justify;\">\u003Cstrong>For more information\u003C\u002Fstrong>: \u003Ca href=\"http:\u002F\u002Fwww.sandisk.com\">www.sandisk.com\u003C\u002Fa>\u003C\u002Fp>","https:\u002F\u002Fmedia.magicwandmedia.in\u002Fposts\u002FJune2026\u002Fnews\u002Fhigh-bandwidth-flash-a-new-memory-for-ai-data-centers-and-edge-computing-lLYZOz.jpg","https:\u002F\u002Fmedia.magicwandmedia.in\u002Fsocial\u002Fhorizontal\u002Fposts\u002FJune2026\u002Fnews\u002Fhigh-bandwidth-flash-a-new-memory-for-ai-data-centers-and-edge-computing-lLYZOz.jpg","2026-06-13T09:42:23+05:30","MWM Desk","News",0,"Current DRAM and HBM architectures are struggling to scale with the exploding capacity and density demands of modern AI models.\nAI inference workloads require a specialized memory architecture that prioritizes cost-effective scalability over traditional low-latency demands.\nHigh Bandwidth Flash (HBF) emerges as a purpose-built, thermally stable solution designed to optimize AI inference and reduce power consumption.",null,"DRAM and HBM are hitting their limits. Is High Bandwidth Flash the game-changer for AI inference? The future of data centers is shifting. #AI #DataCenterTech","Is your memory tech holding back your AI? 🤖💾\n\nAI inference is changing the game, and conventional DRAM and HBM are struggling to keep up with the demand for higher density and lower power. We’re taking a deep dive into the rise of High Bandwidth Flash (HBF)—a new, purpose-built architecture designed to fuel the next wave of data-intensive AI at the edge and in the enterprise. 🚀\n\nIs it time to rethink your data architecture? Check out the full breakdown on our blog! 📈\n\n#MagicWandMedia #AI #DataCenter #EdgeComputing #TechInnovation #StorageSolutions #FutureOfTech",{"title":6,"description":8,"keywords":21},"high bandwidth flash,ai infrastructure,data center memory,ai inference,storage architecture,hbm,dram,edge computing,enterprise ai,memory technology",{"title":23,"slug":24,"imageUrl":25},"Honeywell Introduces Experion Cognition to Deliver Autonomous Control Room Operations for Borouge International","honeywell-introduces-experion-cognition-to-deliver-autonomous-control-room-operations-for-borouge-international","https:\u002F\u002Fmedia.magicwandmedia.in\u002Fposts\u002FJune2026\u002Fnews\u002Fhoneywell-introduces-experion-cognition-to-deliver-autonomous-control-room-operations-for-borouge-international-5hP3mb.jpg",{"title":27,"slug":28,"imageUrl":29},"NXP’s Latest Single-Chip Radar Solution Brings On-Sensor L2\u002FL2+ Adas Processing to Mainstream Vehicle Platforms ","nxps-latest-single-chip-radar-solution-brings-on-sensor-l2l2-adas-processing-to-mainstream-vehicle-platforms","https:\u002F\u002Fmedia.magicwandmedia.in\u002Fposts\u002FJune2026\u002Fnews\u002Fnxps-latest-single-chip-radar-solution-brings-on-sensor-l2l2-adas-processing-to-mainstream-vehicle-platforms-JjYp4q.jpg",{"data":31,"links":104,"meta":108},[32,38,44,50,56,62,68,74,80,86,92,98],{"title":33,"slug":34,"excerpt":35,"imageUrl":36,"publishedAt":37,"author":13,"category":14},"MANNOL Highlights Engine Oil Challenges for Hybrid Powertrains","mannol-highlights-engine-oil-challenges-for-hybrid-powertrains","Discover how hybrid powertrain dynamics are shifting lubrication needs. MANNOL explores the technical challenges and innovative additive solutions for hybrid engine longevity.","https:\u002F\u002Fmedia.magicwandmedia.in\u002Fposts\u002FJuly2026\u002Fnews\u002Fmannol-highlights-engine-oil-challenges-for-hybrid-powertrains-Pg2wpv.jpg","2026-07-17T01:58:14+05:30",{"title":39,"slug":40,"excerpt":41,"imageUrl":42,"publishedAt":43,"author":13,"category":14},"Vembu’s BDRShield Powers RainDrive, a Managed Backup & Disaster Recovery Service  ","vembus-bdrshield-powers-raindrive-a-managed-backup-disaster-recovery-service","Discover how Vembu and INDSYS Techservices launched RainDrive, a new managed backup and disaster recovery service for Indian businesses.","https:\u002F\u002Fmedia.magicwandmedia.in\u002Fposts\u002FJuly2026\u002Fnews\u002Fvembus-bdrshield-powers-raindrive-a-managed-backup-disaster-recovery-service-4JFWox.png","2026-07-17T01:33:58+05:30",{"title":45,"slug":46,"excerpt":47,"imageUrl":48,"publishedAt":49,"author":13,"category":14},"Epson Strengthens India’s Textile Manufacturing Ecosystem with New Industrial Printing Solutions","epson-strengthens-indias-textile-manufacturing-ecosystem-with-new-industrial-printing-solutions","Epson expands its India textile portfolio with the new SureColor SC-G9030 DTF and SC-F20030 dye-sub printers, driving high-speed, sustainable manufacturing.","https:\u002F\u002Fmedia.magicwandmedia.in\u002Fposts\u002FJuly2026\u002Fnews\u002Fepson-strengthens-indias-textile-manufacturing-ecosystem-with-new-industrial-printing-solutions-e19voe.jpg","2026-07-17T01:20:30+05:30",{"title":51,"slug":52,"excerpt":53,"imageUrl":54,"publishedAt":55,"author":13,"category":14},"Hindustan Zinc’s Shiksha Sambal Empowers 47,000+ Students Across Rajasthan Since 2018","hindustan-zincs-shiksha-sambal-empowers-47000-students-across-rajasthan-since-2018","Discover how Hindustan Zinc’s Shiksha Sambal initiative is transforming rural education in Rajasthan with a 100% board exam pass rate.","https:\u002F\u002Fmedia.magicwandmedia.in\u002Fposts\u002FJuly2026\u002Fnews\u002Fhindustan-zincs-shiksha-sambal-empowers-47000-students-across-rajasthan-since-2018-oqBqlW.jpg","2026-07-17T00:25:40+05:30",{"title":57,"slug":58,"excerpt":59,"imageUrl":60,"publishedAt":61,"author":13,"category":14},"Rubix Report Sees Capacity Expansion and Green Steel Driving India’s Steel Growth","rubix-report-sees-capacity-expansion-and-green-steel-driving-indias-steel-growth","Discover how India’s steel sector is targeting 500 MTPA by 2047, driven by massive infrastructure growth and a new green steel focus.","https:\u002F\u002Fmedia.magicwandmedia.in\u002Fposts\u002FJuly2026\u002Fnews\u002Frubix-report-sees-capacity-expansion-and-green-steel-driving-indias-steel-growth-uq5va1.jpeg","2026-07-17T00:18:06+05:30",{"title":63,"slug":64,"excerpt":65,"imageUrl":66,"publishedAt":67,"author":13,"category":14},"TKIL Industries Announces Gaurav Kumar Srivastava as CFO","tkil-industries-announces-gaurav-kumar-srivastava-as-cfo","TKIL Industries appoints Gaurav Kumar Srivastava as the new CFO effective June 2026, succeeding veteran leader Ketan Pendse.","https:\u002F\u002Fmedia.magicwandmedia.in\u002Fposts\u002FJuly2026\u002Fnews\u002Ftkil-industries-announces-gaurav-kumar-srivastava-as-cfo-T5qjO0.jpg","2026-07-17T00:07:54+05:30",{"title":69,"slug":70,"excerpt":71,"imageUrl":72,"publishedAt":73,"author":13,"category":14},"SFK Group Crosses INR 150 Cr Annual Turnover, Expands Industrial Automation Portfolio","sfk-group-crosses-inr-150-cr-annual-turnover-expands-industrial-automation-portfolio","SFK Group achieves INR 150 Cr turnover! 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