How MLCCs and polymer tantalum capacitors support AI servers

https://lnkd.in/g9w77jH7 "Key to this is a level of coherency between the CPUs and GPGPUs, which allows data to be shared seamlessly between compute elements. This reduces unnecessary data movement, improving responsiveness while cutting power use."

The Micron 7600 MAX is built for heavy write enterprise workloads. We put the drive through our lab to see how its endurance, latency consistency, and power profile stack up for databases, caching tiers, and mixed IO. Key takeaways • Write intensive “MAX” SKU targets high DWPD use with strong sustained performance • Excellent latency stability and QoS under mixed and steady-state loads • Competitive performance per watt for dense servers and constrained racks • Clear fit for databases, logging, analytics scratch, and AI data staging Read the full review: https://lnkd.in/ghju5U2B #Micron #NVMe #SSD #DataCenter #StorageReview Micron Technology

The Micron 9550 MAX targets balanced performance for AI staging, databases, and analytics. We ran it through mixed IO and sustained loads to assess its performance and determine its fit. Key takeaways: • Strong read and write balance with predictable QoS under pressure • Low and steady latency that protects SLAs during peak windows • Solid performance per watt for dense servers and edge racks • Enterprise features covered: NVMe 2.0, robust telemetry, security options • Clear fit for DB logs, hot datasets, and AI feature stores Read the full review: https://lnkd.in/g_KFnh-7 #Micron #NVMe #SSD #DataCenter #StorageReview Micron Technology

Users complain about slow page loads, API response delays, and soaring bandwidth costs, yet few realize the bottleneck might lie in gzip—a compression algorithm that has faithfully served for decades. While it was once sufficient, gzip now struggles to keep pace with today's dynamic content and high-concurrency demands. That's why we've integrated the modern compression algorithm zstd into OpenResty Edge. By leveraging lower CPU overhead, we achieve higher compression ratios and faster transmission speeds. Curious about how to embrace next-generation compression technology in OpenResty Edge? This article has the answers you're looking for: https://lnkd.in/gS8zrGKj

The Data center GPU market is on track to grow from $21.6B in 2025 to $265.5B by 2035. (source: Data Center GPU Market Size and Share Forecast Outlook 2025 to 2035) Exploding demand for GPUs means some AI teams wait weeks (or months) for access to enterprise-grade infrastructure. This creates a variety of pipeline-to-production issues: - Shared resources (versus dedicated hardware) slow down experiments and delay model deployments. - Without guaranteed compute, R&D pipelines stall. They can skip the wait when they partner with Voltage Park: ✔️ We've expanded our fleet of H100s to now include Blackwell clusters - no waiting in line. ✔️ No hyperscaler lock-in: scale up or down instantly based on your needs. ✔️ Bare-metal access for maximum performance, not throttled VMs. Our website: https://bit.ly/4lnrQTp

Generative #AI is revolutionizing the way we think about compute power—and with it, data center cooling strategies. The latest GPU-accelerated servers consume up to 20x the power of standard CPU servers, generating immense heat loads that can only be managed with liquid cooling. But while liquid cooling is essential, it’s not without its challenges. Check out this blog by Steven Carlini for a deeper dive: http://spr.ly/6040A7zuO

🚀 New #MLPerf Storage v2.0 results are in! #JuiceFS delivers top-tier performance for #AITraining: ✅ Supports up to 500 H100 GPUs ✅ Achieves 72% #BandwidthUtilization on Ethernet (far exceeding the ~40% seen from other vendors) See the analysis: https://lnkd.in/gbrRVYF9 #DistributedFileSystem #DistributedStorage #DataStorage #AIStorage #DataPerformance #LLM #AISolutions #ArtificialIntelligenceStorage

It looks like the latest Beta 5 of RAS 6.7 has fully enabled the ability to test your runtime while varying the number of cores: https://lnkd.in/gzCq6q6s It's a cool feature, and I think I can save everyone some time by sharing a quite easy and consistent rule of thumb that can be applied for 2D models above a certain practical threshold of size where runtimes become significant: HEC-RAS Core Scaling Rules of Thumb 2 cores = Most Efficient 4 cores = Best Balance 8-16 cores = Best Performance (depending on model and CPU, assuming you have that many) From 1-2 cores is linear scaling, so you should generally always run at least 2 cores. I haven't seen a processor scale linearly from 1 to 4 cores, there's always a marginal efficiency decrease. 8-16 cores is where you will find the minimum runtime, usually 50-80% faster than running at 2 cores (note that this is after applying 4-8x the compute effort). After you pass 8-16 cores, you can typically see a penalty of 10% or more for using *too many* cores. The take home is that gains past 4 cores become increasingly marginal and even if you can eke out a slightly lower runtime at say, 24 or 48 cores on a specific architecture, it likely won't be more than 20% faster than at the optimum performance point, in the 8-16 core range. Once you are looking at large run sets, long run times, and large core counts, the cost footprint becomes quite significant, and applying orders of magnitude of increasing compute time for marginal performance gains is not always advisable. That's why I created notebooks to enable ad-hoc parallelization of HEC-RAS, to enable modelers to get up to ~50-70% gains in throughput per core utilized (by using 2 cores instead of 8), plus the ability to double, triple, etc the number of applied cores for multi-run sets by adding networked workstations to a common execution pool. This was intended to sidestep this nonlinear compute scaling behavior as much as is technically feasible in a non-cloud environment. Plus publishing the core scaling benchmarking that I had done, to help clear up confusion for those who are scaling in the cloud. I love that this feature has been added, because everyone will be able to test with their own models and hardware and confirm their own findings which should be quite consistent with those previously published in the HEC-Commander blog. It's quite reproducible behavior. Even there are edge cases, the above rules of thumb hold roughly true for the vast majority of x86 hardware: consumer, enterprise or cloud. Note: If you have hyperthreading enabled, all of the above numbers are doubled. In fact, benchmarking at 1, 2 and 4 cores can indirectly indicate whether your cloud VCPU's may actually be hyperthreaded.

Insightful read from @AMD’s Shiva Gurumurthy on how high-frequency processors are unlocking new levels of efficiency and simplifying infrastructure for long-term business success. Discover how AMD EPYC CPUs are driving smarter AI and data center performance by optimizing GPU utilization and reducing latency — all while aligning with evolving enterprise goals. https://bit.ly/3LD1kJu #AMD #EPYC #AIInfrastructure #DataCenter #HighFrequencyCPUs #Efficiency #Innovation

AIC’s SB407-VA is a 4U storage server built for heavy duty data demands in AI pipelines, analytics, backup, media, and surveillance. We break down the design choices that make it practical to deploy and scale. What we cover: • Capacity and bay layout with flexible NVMe, SAS, and SATA options • Platform choices for CPUs, memory, and PCIe Gen5 expansion for high speed NICs or accelerators • Data protection paths using RAID or software defined stacks • Power, airflow, and serviceability details for dense racks Read the full story: https://lnkd.in/gBg89VEH #AIC #StorageServer #DataCenter #AIInfrastructure #NVMe #PCIe5 #EnterpriseIT #StorageReview AIC Inc.