130 summarised stories about AI Accelerators, each linking back to the original source. Browse all topics →
SiliconANGLE 2 months ago 27
SK hynix approved construction of two new memory fabs to expand its manufacturing capacity. The first initiative will cost 54 trillion South Korean won (about $38.3 billion) and includes a $25 billion DRAM fab called Yongin Y2. Construction timelines move forward, with Yongin Y2 cleanroom work starting in June 2029 and a separate NAND flash fab aiming for a first cleanroom in December 2028.
EEJournal 2 months ago 9
Blaize has developed a Graph Streaming Processor (GSP) designed to reduce unnecessary data movement during AI inference by scheduling computations to keep intermediate data flowing through the processor rather than shuttling it to external memory. The company positions its GSP for the first stage of multi-stage inference pipelines, handling routine tasks efficiently while routing complex cases to GPUs, with the same silicon packaged across embedded modules, PCIe cards, and rack-mount servers. This approach reduces power consumption and hardware costs by matching processor type to workload complexity rather than using expensive GPUs for every computation.
The Register 2 months ago 30 ● 2 sources
AMD acquired Taalas, a startup that embeds AI model weights directly into silicon to accelerate inference. Taalas's approach delivers performance improvements of 10x or greater compared to standard inference. The acquisition gives AMD proprietary technology to optimize its AI chip offerings against competitors like NVIDIA.
Latent Space 2 months ago 44 ● 2 sources
AMD acquired Taalas, a custom AI chip company, according to CEO Lisa Su's decision to pursue vertical integration for inference workloads. The article does not provide a specific acquisition price or date. The move signals AMD's commitment to custom silicon for AI inference, following industry trends toward vertical specialization and custom ASICs rather than general-purpose accelerators.
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17 October 2026
Microsoft used today’s announcement to zoom in on what matters in scaling: not just raw capability, but the plumbing behind it. The company revealed MAI-Base-2, a 2T-parameter base model designed to be converted into full LLMs with additional training. Per Microsoft, only 80B parameters activate per prompt, and the training run ate 50T tokens over 38 days using 16,000 Nvidia GB200 GPUs. The headline claim: MAI-Base-2 is up to 12× more hardware-efficient than its predecessors, a metric that will matter to anyone trying to keep inference costs from swallowing margin.
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