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RCRTech engages industry communities through research-driven content, conversations, and connections. Building on 40+ years of RCR Wireless News excellence, RCRTech delivers trusted insights informing and connecting technology buyers with innovators shaping connectivity and compute.
RCRTech engages industry communities through research-driven content, conversations, and connections. Building on 40+ years of RCR Wireless News excellence, RCRTech delivers trusted insights informing and connecting technology buyers with innovators shaping connectivity and compute.
Juan Pedro TomásMicrosoft launched its first AI-focused chip, the Azure Maia accelerator, and the Cobalt CPU in 2023, and is working on next-generation versions In sum – what to know: Microsoft plans chip self-reliance – CTO Kevin Scott said the firm ultimately wants its own silicon to power AI data centers, reducing reliance on Nvidia and AMD. End-to-end system design focus – …
OpenAI also signed a MoU with the Ministry of Science and ICT to assess opportunities for data center facilities outside the Seoul metropolitan area In sum – what to know: Expanded chip capacity in Korea – Samsung and SK hynix aim to scale memory production to 900,000 DRAM wafer starts per month to meet growing AI model demands. New data …
KT plans to roll out the model first within its consumer services, before expanding to enterprise clients following stability checks In sum – what to know: Korea-tailored AI model – KT introduced SOTA K, built on GPT-4o with Microsoft, designed to reflect Korean language, grammar, and cultural context. Outperforms global models in benchmarks – SOTA K exceeded GPT-4o in Korean-specific …
Christian de LooperBuying up old GPUs for AI might be the way to go for some smaller AI outfits Every time Nvidia drops a new flagship accelerator, the entire AI processing landscape reshapes practically overnight. Hyperscalers scramble for allocation and last generation’s hardware gets treated like it’s ancient history. But what doesn’t get anywhere near as much attention is the outgoing generation of accelerators — which …
New 102.4 tbps Silicon One promises efficiency gains for hyperscalers In sum – what we know: It’s easy to focus on the GPU arms race when talking about AI infrastructure, but the networking connecting all those accelerators is arguably almost as important. You can rack up as many GPUs as your budget allows — but if the data can’t shuttle between them …
How can quantization turn massive models into efficient tools without ruining their accuracy? Running large language models is expensive. The biggest ones pack hundreds of billions of parameters, each stored as a high-precision number that chews through memory, power, and premium hardware. But do we actually need all that precision? Increasingly, the answer is no. That realization has driven the …
Replacing copper with optical pipes could have a significant impact on the AI data bottleneck The semiconductor industry has been following the same steps for decades, revolving around shrinking the transistor and packing them more into a chip. It’s worked remarkably well. But there’s a problem emerging that transistor density simply can’t fix — getting data from point A to …
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