#mlperf

[fraoula1]

7 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬 𝐟𝐨𝐫 𝐔𝐧𝐛𝐢𝐚𝐬𝐞𝐝 𝐀𝐈 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐑𝐞𝐯𝐢𝐞𝐰𝐬 𝐘𝐨𝐮 𝐍𝐞𝐞𝐝 𝐭𝐨 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐢𝐧 2025

Looking for trustworthy platforms to evaluate deep tech AI products? We just published a data-backed guide to the Top 7 Verified Review Platforms every CTO, AI strategist, and enterprise buyer should know.

Packed with research metrics, industry benchmarking data (MLPerf, Hugging Face, Stanford AI Index), and verified user insights — this is your go-to resource for making confident AI product decisions in 2025.

Read the full blog here: https://www.fraoula.co/post/7-essential-research-platforms-for-unbiased-ai-product-reviews-you-need-to-explore-in-2025

[govindhtech]

Intel MLPerf: Benchmarking Hardware For Machine Learning(ML)

Overview

This briefing describes Intel MLPerf, a popular and rapidly growing benchmark suite for machine learning (ML) hardware, software, and services. Intel MLPerf, formed by a wide coalition of academic, scientific, and industry organisations, compares ML systems impartially to accelerate innovation. MLPerf's definition, operation, aims, and relevance in artificial intelligence will be discussed in this article.

What's MLPerf?

When combined, “ML” for machine learning and “Perf” for performance create “MLPerf.” MLPerf is a series of benchmarks that evaluates ML systems in different tasks and conditions.

Intel MLPerf, an industry benchmark, measures ML hardware and software performance. It standardises machine learning system evaluation and progress tracking.

MLPerf emphasises real-world application settings rather than vendor-specific criteria to level the playing field for machine learning performance assessment. Developers, researchers, and consumers may pick the best hardware and software for their machine learning needs.

How MLPerf Works

MLPerf's rigorous and transparent process involves several key elements:

Benchmark Suites: Intel MLPerf has several benchmark suites for specific ML problems. Over time, these suites evolve with the field. Edge computing, inference, and training examples are given.

Machine learning concerns including recommendation systems, object recognition, picture classification, and NLP are addressed.

Open Participation: The Intel MLPerf collaboration welcomes cloud service providers, software developers, hardware manufacturers, and educational organisations. This coordinated approach ensures benchmark applicability and credibility.

Standardised Rules and indicators: MLPerf sets strict benchmarking standards and performance metrics to ensure fair comparisons. The rules cover allowed optimisations, model accuracy targets, and data preparation.

Benchmarks include strict requirements to provide fair system comparisons.

After participants submit their performance results, the MLPerf website posts complete software stacks and system specifications for public review. This transparency encourages healthy rivalry and clear comparisons. Leaderboards are crucial for tracking progress:

Users may see how different systems perform on different machine learning workloads because the findings are publically available.

Focus on Practical Tasks: Intel MLPerf benchmarks simulate genuine ML applications using representative or public datasets. It ensures that performance indicators apply to real-world use cases.

The Value of MLPerf

The Intel article emphasises many aims and MLPerf's role in the AI ecosystem:

Objective Comparisons: MLPerf simplifies machine learning system comparisons by standardising methods and metrics. This lets customers make data-driven choices.

MLPerf sets defined performance objectives and makes hardware and software innovations public to motivate vendors to improve results. Competition increases growth.

Open submission and comprehensive reporting standards make ML performance claims transparent. Users may view software stacks and settings used to achieve goals.

Influencing Purchase Decisions: Intel MLPerf findings assist organisations adopt ML solutions by revealing the performance capabilities of different hardware and software alternatives for specific workloads.

Monitoring Development in the Field: MLPerf results indicate how new algorithms, software optimisations, and architectural upgrades affect ML system performance over time.

It tracks ML technology advancement.

MLPerf benchmarks training and inference at many levels of ML. This provides a complete system performance view.

The Changes and Impact of MLPerf

Remember that MLPerf is a dynamic project that extends beyond description and operation.

New ML tasks, models, and application areas are introduced to benchmark suites often to keep current. Long-term effects depend on adaptability.

The quest for MLPerf benchmark perfection affects hardware and software design, including CPUs, GPUs, memory systems, interconnects, and software frameworks. To meet these standards, companies actively optimise their products.

Community-Driven Development: MLPerf's strength is its community participation. The consortium's transparent and cooperative structure ensures that benchmarks reflect machine learning community concerns.

Addressing Emerging Trends: MLPerf is assessing edge computing, personalised recommendation systems, and massive language models to keep up with AI application changes.

In conclusion

The primary machine learning system effectiveness benchmark is Intel MLPerf. A standardised, transparent, and community-driven evaluation strategy empowers users, stimulates innovation, and facilitates informed decision-making in the fast-growing field of artificial intelligence. MLPerf's development and use are crucial for tracking progress and understanding AI technology potential.

[ourwitching]

NVIDIA's MLPerf Training V4.0 is out. It is mostly NVIDIA H100 and H200 so if you are looking to com...

[3acesnews]

NVIDIA's Blackwell Architecture Sets New Performance Standards in MLPerf Training

[fabiopempy]

CoreWeave, NVIDIA, And IBM Submit Record-Breaking MLPerf Results Using NVIDIA GB200 Grace Blackwell Superchips

Software cloud platform CoreWeave announced that it has collaborated with NVIDIA and IBM to complete the largest MLPerf Training v5.0 submission to date using NVIDIA Blackwell technology. The effort utilized 2,496 NVIDIA Blackwell GPUs operating on CoreWeave’s cloud infrastructure, which is optimized for AI. This benchmark represents the most extensive NVIDIA GB200 NVL72 cluster evaluated

Read More: You won't believe what happens next... Click here!

[groovy-computers]

🚀 Ready to revolutionize your AI infrastructure? Super Micro's NVIDIA HGX B200 systems are leading the charge with stunning AI performance leaps. 🔍 Context: Our latest 8-GPU systems offer a groundbreaking 3.1x throughput boost for Llama2-70B, outpacing H200 setups in MLPerf v5.0 benchmarks. Imagine generating 98,443 tokens per second compared to just 33,072 – that's the power of our 4U liquid-cooled and 10U air-cooled systems. 💡 Dive into the Details: With consistently verified, production-ready hardware, our rack configurations allow up to 96 Blackwell GPUs, promising a **15x performance gain.** Perfect for those seeking high-density, efficient AI processing solutions. 🔗 Insight: Staying ahead in AI means embracing innovation today. Our systems offer accessibility, scalability, and power, reshaping the industry's pace. What's your take on the evolving AI landscape? Let us know below! 👇 #AI #SuperMicro #GPU #TechInnovation #MachineLearning #LLAMA2 #BlackwellGPUs #HighPerformanceComputing 🌐

[dr-iphone]

NVIDIA B200 登場！Supermicro AI 伺服器效能狂飆 3 倍，測試資料公開

Nvidia HGX B200 Systems Supermicro 近期發表最新 AI 伺服器，搭載 NVIDIA HGX B200 8-GPU，在 MLPerf Inference v5.0 測試中展現壓倒性優勢，效能比前一代 H200 提升 3 倍以上。 Continue reading NVIDIA B200 登場！Supermicro AI 伺服器效能狂飆 3 倍，測試資料公開

[infernovm]

New MLCommons benchmarks to test AI infrastructure performance

Industry consortium MLCommons has released new versions of its MLPerf Inference benchmarks, offering a closer look at how current-generation data center and edge hardware performs under increasingly demanding AI workloads. The updated MLPerf Inference V5.0 comes as infrastructure teams grapple with surging demand from generative AI applications like chatbots and code assistants, which require…

[strategictech]

MLCommons Releases MLPerf Inference v5.0 Benchmark Results

Today, MLCommons announced new results for its MLPerf Inference v5.0 benchmark suite, which delivers machine learning (ML) system performance benchmarking. The rorganization said the esults highlight that the AI community is focusing on generative AI, and that the combination of recent hardware and software advances optimized for generative AI have led to performance improvements over the past year.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[jcmarchi]

Singapore-based Firmus wins recognition for AI data centre design

New Post has been published on https://thedigitalinsider.com/singapore-based-firmus-wins-recognition-for-ai-data-centre-design/

Singapore-based Firmus wins recognition for AI data centre design

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Singapore-based Firmus Technologies has been recognised with the Asia Pacific Data Centre Project of the Year award for its AI Factory facility.

The facility stands out for its advanced infrastructure and focus on energy efficiency, reflecting broader efforts to meet the rising demands of AI computing sustainably.

The AI Factory is part of Firmus’s ongoing initiative to transform existing ST Telemedia Global Data Centres (STT GDC) into GPU-powered AI computing platforms. The redesigned centres are equipped with state-of-the-art hardware and efficient cooling systems, enabling them to meet both enterprise and research needs with improved energy performance metrics.

As artificial intelligence continues to need more power, energy efficiency has become a major issue. Firmus has addressed the issue for nearly a decade with its AI Factory platform, which combines advanced immersion cooling technology with dependable design, build, and operation services. The company states its platform has several significant advantages, including:

Energy efficiency: 45% more FLOP per utility picoJoule than traditional data centres,

Cost-effectiveness: Up to 30% cheaper total cost of ownership (TCO) than direct-to-chip cooling platforms,

Scalability and sustainability: Supports high-density AI workloads while reducing environmental effects,

Global expertise: A track record in building and operating immersion-cooled data centres in Singapore and Australia.

The deployment of the AI Factory in Singapore shows how innovative approaches to data centre infrastructure can address the energy demands of AI. The project highlights a potential pathway for sustainable AI development by achieving a pPUE of 1.02 and a reduction in energy consumption of 45%. The achievement aligns with Singapore’s National AI Strategy 2.0, which emphasises sustainable growth in AI and data centre innovation.

Tim Rosenfield, co-CEO of Firmus Technologies, explained the broader vision behind the project, noting that it’s about balancing AI growth with sustainability. “By rethinking data centre design, we have created a platform that supports the growth of AI while promoting environmental sustainability. If we can do it in Singapore, where space is constrained and the humid climate is against us, we can do it anywhere,” he said.

Firmus has recently changed its leadership team, adding Dr. Daniel Kearney as chief technology officer. Previously AWS’s Head of Technology for the ASEAN Enterprise business, Kearney leads the engineering team at Firmus. He pointed out how sustainable AI infrastructure is becoming essential as AI technologies expand. “This win against established data centre players recognises the importance of technology like ours in meeting the growth of AI and the energy challenges it brings,” he said.

The company has been advancing its work through the Sustainable Metal Cloud (SMC), an initiative aimed at improving the efficiency and sustainability of AI infrastructure. Recent updates from Firmus include:

Power efficiency benchmarks: Firmus became the first to publish comprehensive power consumption data alongside performance results for the MLPerf Training benchmark,

Policy contributions: Insights from Tim Rosenfield contributed to the Tony Blair Institute for Global Change’s policy agenda on managing the energy demands of the AI sector,

Industry discussions: At ATxSG24, Firmus’s Chairman, Edward Pretty, joined a panel featuring organisations like NVIDIA, the World Bank, and Alibaba Cloud to explore the balance between sustainability and the computational needs of AI,

Hypercube expansion: Firmus’s team of 700 is installing the first fleet of Sustainable AI Factories, known as HyperCubes in multiple regions.

Engagement at NVIDIA GTC 2024: The company participated in two panels at NVIDIA’s GTC event, discussing sustainable AI infrastructure alongside partners like NVIDIA, Deloitte, and WEKA.

See also: The AI revolution: Reshaping data centres and the digital landscape 

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: artificial intelligence, data centre

[govindhtech]

Micron 6500 ION SSD: Turn AI with 256 Accelerators

Micron 6500 ION SSD

Results for MLPerf Storage v0.5 on the Micron 9400 NVMe SSD were just released by Micron. These outcomes demonstrate how effectively the Micron 9400 NVMe SSD performs in the use case of an AI server as a local cache, thanks to its high-performance NVMe SSD. The majority of AI training material, however, is stored on shared storage rather than in local cache. The identical MLPerf Storage AI workload was chosen to be tested for SC23 on a WEKA storage cluster that was powered by a 30TB Micron 6500 ION SSD.

They were interested in learning how the MLPerf Storage AI application scaled on a high-performance SDS solution. WEKA is a distributed, parallel filesystem built for AI workloads. The results are insightful, pointing to the need for huge throughput in future AI storage systems and assisting us in sizing suggestions for current-generation AI systems.

Let’s quickly review MLPerf Storage first

In order to facilitate the creation of future state-of-the-art models, MLCommons creates and maintains six distinct benchmark suites in addition to accessible datasets. The most recent addition to the MLCommons benchmark collection is the MLPerf Storage Benchmark Suite.

MLPerf Storage aims to tackle several issues related to the storage workload of AI training systems, including the limited size of available datasets and the high expense of AI accelerators.

See these earlier blog entries for a detailed analysis of the benchmark and the workload produced by MLPerf Storage:

Regarded as the best PCIe Gen4 SSD for AI storage, the Micron 9400 NVMe SSD

MLPerf Storage on the Micron 9400 NVMe SSD: storage for AI training

Let’s now discuss the test WEKA cluster

Earlier this year, they colleague Sujit wrote a post outlining the cluster’s performance in synthetic workloads.

Six storage nodes comprise the cluster, and the configuration of each node is as follows:

The AS-1115CS-TNR Supermicro

Processor AMD EPYC 9554P single-socket

64 cores, 3.75 GHz boost, and 3.1 GHz base

Micron DDR5 DRAM, 384 GB

30TB, 10 Micron 6500 ION SSDs

400 GbE networking

This cluster can handle 838TB of capacity overall and can reach 200 GB/s for workloads with a high queue depth.

Let’s now take a closer look at this cluster’s MLPerf Storage performance

A brief note: Since the data have not been submitted for evaluation to MLPerf Storage, they are unvalidated. Changes are also being made to the MLPerf Storage benchmark from version 0.5 to the upcoming version for the first 2024 release. Utilizing the same methodology as the v0.5 release, the values displayed here share a barrier between accelerators in a client and independent datasets for each client.

In the 0.5 version, the MLPerf Storage benchmark simulates NVIDIA V100 accelerators. There are sixteen V100 accelerators on the NVIDIA DGX-2 server. The number of clients supported for this testing is displayed on the WEKA cluster, where each client simulates 16 V100 accelerators, similar to the NVIDIA DGX-2.

Furthermore, Unet3D and BERT, two distinct models, are implemented in MLPerf Storage benchmark version 0.5. Testing reveals that BERT does not produce a substantial amount of storage traffic, hence the testing here will concentrate on Unet3D.

Micron 6500 ion ssd specs

For a specific number of client nodes, the overall throughput to the storage system is displayed in this graphic. Recall that there are 16 emulated accelerators on each node. Additionally, for a given number of nodes and accelerators to be deemed a “success,” they must maintain an accelerator utilization rate of greater than 90%. The accelerators are idle while they wait for data if their percentage falls below 90%.

The six-node WEKA storage cluster can handle 16 clients, each of which can imitate 16 accelerators, for a total of 256 emulated accelerators, and achieve a throughput of 91 GB/s.

With 16 V100 GPUs per system, this performance is equivalent to 16 NVIDIA DGX-2 systems, which is an astonishingly large number of AI systems powered by a six-node WEKA cluster.

The V100 is a PCIe Gen3 GPU, and NVIDIA’s GPU generations are advancing at a rate that is far faster than PCIe and platform generations. They discover that an emulated NVIDIA A100 GPU performs this workload four times quicker in a single-node system.

They may calculate that this WEKA deployment would handle eight DGX A100 systems (each with eight A100 GPUs) at a maximum throughput of 91 GB/s.

Future-focused AI training servers, such as the H100 / H200 (PCIe Gen5) and X100 (PCIe Gen6) models, are expected to push extremely high throughput.

As of right now, the Micron 6500 ION SSD and WEKA storage offer the ideal balance of scalability, performance, and capacity for your AI workloads.

Read more on Govindhtech.com

[ourwitching]

NVIDIA's MLPerf Training V4.0 is out. It is mostly NVIDIA H100 and H200 so if you are looking to com...

[3acesnews]

CoreWeave Achieves Record MLPerf Benchmark with NVIDIA GB200 Superchips