Artificial Intelligence News - Page 3

Geekbench AI 1.0 is here, after years of feedback and test iteration with its customers, partners, and the AI engineering community, Primate Labs is proud to announce its latest machine learning benchmark is now ready, and it has a new name: Geekbench AI. You can download Geekbench AI 1.0 here.

Geekbench AI is a new benchmarking suite with a testing methodology for machine learning, deep learning, and AI-centric workloads, all with the same cross-platform utility and real-world workload reflection that Primate Labs' benchmarking software (Geekbench, duh) is known for.

The developer explains on its website: "Measuring performance is, put simply, really hard. That's not because it's hard to run an arbitrary test, but because it's hard to determine which tests are the most important for the performance you want to measure - especially across different platforms, and particularly when everyone is doing things in subtly different ways. At Primate Labs, we build our tests to reflect the sort of use cases that developers build their applications to do through detailed and ongoing conversations with software and hardware engineers across the industry, rather than just crunching basic math for hours".

SoftBank was talking with Intel about making an AI chip that would compete with NVIDIA's dominant AI GPUs, but the plan "floundered" after Intel failed to meet the requirements of Japan's SoftBank.

In a new report from the Financial Times, we're hearing that negotiations to partner with Intel would've rapidly accelerated SoftBank's efforts to combine the chip design of its "crown jewel" as the FT puts it: Arm. Japan's SoftBank owns Arm Holdings, so with its Arm architecture and Intel fabbing the chip with SoftBank's production expertise of its latest acquisition -- Graphcore -- according to "people familiar with the matter".

SoftBank boss Masayoshi Son plans to dump billions of dollars into putting Japan at the center of the AI boom, where the Arm owner wants to create a rival to NVIDIA's dominant AI GPUs. Son has pitched his ideas to the usual Big Tech companies, with chip production and software through to providing power for the data centers that would be powered by its chips.

Samsung's new 8-layer HBM3E memory has passed NVIDIA's qualification tests to be used in its AI GPUs.

In a new report from Reuters, the outlet says that the qualification clears a "major hurdle" for Samsung -- which is the world's largest memory manufacturer -- and has been struggling to catch up to South Korean rival, SK hynix, which has been providing its bleeding-edge HBM3E memory to NVIDIA for its AI GPUs.

Samsung still hasn't signed a supply deal for the approved 8-layer HBM3E memory chips with NVIDIA, but "will do so soon" according to Reuters' sources, who declined to be identified as the matter remains confidential. The new 8-layer HBM3E memory ships from Samsung would be supplied to NVIDIA in Q4 2024, which is not far away now.

The world of AI PCs is just beginning with the first waves of Copilot+ ready PCs out in the wild, with 14% of PCs shipped globally in Q2 2024 featuring an NPU, making them an AI PC.

In a new report from Canalys, we're learning that 8.8 million AI-capable PCs were shipped in Q2 2024, with each of those 8.8 million systems featuring the latest AI processor (with an NPU) from AMD, Apple, Intel, and Qualcomm. As we see new generations of AI processors from AMD and Intel, Canalys says that we can expect rapid growth in the second half of this year, and going into high gear in 2025.

If we look at just the Windows segment, AI-capable PC shipments surged 127% sequentially in Q2 2024, with Lenovo shipping out its Qualcomm Snapdragon X Elite-based Copilot+ PCs with the Yoga Slim 7x and ThinkPad T14s, boosting its AI PC market share to around 6% of all Windows PC shipments, a mammoth 228% growth. HP followed behind Lenovo with a 7% share of AI PCs.

It looks like there is a "component shortage" for NVIDIA's new Blackwell-based GB200 AI servers, with market demand in Taiwan at the highest levels because of the AI market needing as many chips as possible.

In a new report from Taiwan Economic Daily, we're learning that NVIDIA's new GB200 AI servers are experiencing huge development issues, with the main problem stemming from leaks from the liquid cooling system inside of the next-generation multi-million-dollar AI server.

SK hynix has just announced it's planning to develop a 4F2 (square) DRAM, joining South Korean rival Samsung and its journey into the world of 3D DRAM.

The cost of EUV (extreme lithography) processes has continued to skyrocket since the commercialization of 1c DRAM, with SK hynix researcher Seo Jae Wook noted during an industry conference in Seoul, South Korea, on Monday.

The Elec reports that Seo said at the time whether manufacturing DRAM this way (using EUV) was profitable, where in response, SK hynix said it was considering manufacturing vertical gate (VG) or 3D DRAM for future DRAM. VG is what SK hynix internally calls 4F2, while Samsung calls theirs vertical channel transistor (VCT).

Last month we reported on AMD's announcement that it was acquiring the largest private AI lab in Europe - Silo AI. Today, AMD has followed up on the announcement to confirm that the "all-cash transaction valued at approximately $665 million" has now been completed, with Silo AI's scientists and engineers now a part of the AMD family.

$665 million in cash is nothing to sneeze at, and for AMD, the acquisition is the latest step in the company's broader pivot that puts its main focus on AI and AI-related technologies. This is nothing new; we've seen the same shift happen in other companies like Google, Meta, Apple, and, of course, NVIDIA. However, NVIDIA's AI focus started many years ago.

"AI is our number one strategic priority," said Vamsi Boppana, AMD senior vice president, AIG. "We continue to invest in both the talent and software capabilities to support our growing customer deployments and roadmaps."

GPU VRAM modding isn't new, but Chinese cloud companies are adding copious amounts of VRAM to the likes of the GeForce RTX 4090D (24GB modded to 48GB) and the RTX 4080 SUPER from 16GB to 32GB and renting them out at an hourly rate.

Now, we've got a Chinese AI expert that is talking with people in the country about selling NVIDIA's new GeForce RTX 4090D graphics card -- but with double the VRAM, from 24GB to 48GB of GDDR6X -- as well as the GeForce RTX 4080 SUPER upgraded from 16GB to 32GB of GDDR6X memory.

South Korea's memory chip exports to Taiwan surged over 225% year-over-year, thanks to the unstoppable demand of AI GPUs and AI accelerators and their respective HBM memory chips.

In a new report from the Korea Times, we're learning that outbound shipments of HBM memory chips to Taiwan reached $4.26 billion in the first 6 months of this year, up an incredible 225.7% from a year ago, "far outperforming" South Korea's overall increase of memory chip exports at 88.7%, according to data compiled by the industry ministry and the Korea International Trade Association.

Taiwan was the third-largest importer of South Korean memory chips in the period, pushing out both Vietnam and the United States. Market watchers said that the huge surge in memory chip exports to Taiwan is thanks to SK hynix's fantastic HBM supply to NVIDIA, which uses its HBM memory inside of its AI GPUs, and fabs its chips over at TSMC in Taiwan.

NEO Semiconductor has just unveiled the development of its new 3D X-AI chip technology, which aims to replace DRAM chips inside of HBM to solve data bus bottlenecks, by enabling AI processing through 3D DRAM.

The new 3D X-AI chip technology can reduce the huge amount of data transferred between HBM and GPUs during AI workloads, with NEO's innovative new 3D X-AI chip technology set to "revolutionize the performance, power consumption, and cost of AI chips for AI applications like generative AI".

NEO's new 3D X-AI chip technology has 100x the performance with 8000 neuron circuits to perform AI processing in 3D memory, a huge 99% power reduction that minimizes the requirement of transferring data to the GPU for calculation, reducing power consumption and heat generation by the data bus, and 8x the memory density with 300 memory layers, allowing HBM to store larger AI models.