Vol. I · No. 163TUE, SEP 29, 2026
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Search the full wire by company, model, lab, or keyword. Every story we have ever aggregated.

Scaling AI for everyone

$110B funding round at $730B valuation from SoftBank ($30B), NVIDIA ($30B), Amazon ($50B).

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Making Softmax More Efficient with NVIDIA Blackwell Ultra

LLM context lengths are exploding, and architectures are moving toward complex attention schemes like Multi-Head Latent Attention (MLA) and Grouped Query... LLM context lengths are exploding, and architectures are moving toward complex attention schemes like Multi-Head Latent Attention (MLA) and Grouped Query Attention (GQA). As a result, AI ”speed of thought” is increasingly governed not by the massive throughput of matrix multiplications, but by the transcendental math of the softmax function. Transcendentals refer to functions that cannot be… Source

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Using NVFP4 Low-Precision Model Training for Higher Throughput Without Losing Accuracy

As the sizes of AI models and datasets continue to increase, relying only on higher-precision BF16 training is no longer sufficient. Key challenges such as... As the sizes of AI models and datasets continue to increase, relying only on higher-precision BF16 training is no longer sufficient. Key challenges such as training throughput expectations, memory limits, and rising costs are becoming the primary barriers to scaling transformer models. Using lower-precision training can address these challenges. By reducing the numeric precision used during… Source

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Our First Proof submissions

OpenAI submits proof attempts to First Proof math challenge, demonstrating research-grade reasoning on expert-level problems.

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Accelerating Data Processing with NVIDIA Multi-Instance GPU and Locality Domains

NVIDIA flagship data center GPUs in the NVIDIA Ampere, NVIDIA Hopper, and NVIDIA Blackwell families all feature non-uniform memory access (NUMA) behaviors, but... NVIDIA flagship data center GPUs in the NVIDIA Ampere, NVIDIA Hopper, and NVIDIA Blackwell families all feature non-uniform memory access (NUMA) behaviors, but expose a single memory space. Most programs therefore do not have an issue with memory non-uniformity. However, as bandwidth increases in newer generation GPUs, there are significant performance and power gains to be had when taking into… Source

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30 stories