This arXiv paper proposes learning indexical policies with a choose rule to enable heuristic search that runs in polynomial space, trading space for time exponential only in choice depth. This could matter for AI infrastructure by…

#AI #Semiconductors #GPUs
https://arxiv.org/abs/2610.10954

Semiconductor and AI firms are preparing contingency plans for a potential "Day…

#Semiconductors #AIInfrastructure #GPUs #TechNews
https://news.google.com/rss/articles/CBMiW0FVX3lxTE5iOHh2Mmp2ejlOZDVoQzdVMU55dXVoSDBVbEFZbFRtTlpIYlBGTUExcmhMdXE5VjFhMlZzWnZxamhzdmtZZXk2VkNDSUVHMzlDc0JreDRTdDczWEk

A new project demonstrates building a fully open reranker for agentic search from scratch using only a handful of GPUs, avoiding third-party open-weight backbones and relying on common datasets with pretraining plus supervised…

#OpenSource #AI #SemanticSearch #GPUs
https://arxiv.org/abs/2610.11922

MiMo-V2.6 is an omni-modal model family scaling reinforcement learning compute across batch size, environment diversity, and grader compute to advance self-improvement in foundation models. The setup uses async training handling…

#semiconductors #AI #GPUs #MiMo
https://arxiv.org/abs/2610.11959

New arXiv paper introduces ResourceHijackBench, a benchmark with 300 scenarios and 900 prompts for studying LLM agent resource hijacking across GPUs, servers, and APIs.

#Semiconductors #GPUs #AIInfrastructure
https://arxiv.org/abs/2608.15108

New research introduces STATIC, a method that flattens prefix trees into a compressed sparse row matrix to enable efficient constrained decoding for LLM-based generative retrieval on TPUs and GPUs. The approach…

#AIInfrastructure #GPUs #LLMs #RecommenderSystems
https://arxiv.org/abs/2602.22647

New arXiv paper CoMoE tackles the cost barrier of MoE model deployment by enabling efficient inference on consumer GPUs over PCIe, using host-centric routing to bypass the need for expensive NVLink interconnects. If it holds up, this…

#AIInfrastructure #GPUs #MoE
https://arxiv.org/abs/2610.09424

South Korea's 2027 budget sets aside 4.7 trillion won for a single frontier AI model, including 10,000 Nvidia Vera Rubin GPUs, with a new contest opening after budget approval.

#AI #FrontierModel #Gpus #SouthKorea #SovereignAi

#GPUS Hyperscale Data Announces Live Access to Decentralized Exchange on the Ault Blockchain through Ault Markets

https://www.stocktitan.net/news/GPUS/hyperscale-data-announces-live-access-to-decentralized-exchange-on-c3c3k7k784kn.html?utm_source=stocktitan-bluesky&utm_medium=social

TurboPairFormer presents a triangular attention kernel for NVIDIA Hopper GPUs that stabilizes AlphaFold3-style training with a key-tile-parallel backward pass and ordered partial reductions, avoiding floating-point…

#AI #GPUs #Bioinformatics #AIInfrastructure
https://arxiv.org/abs/2610.05854

This paper details a real-time thumbnail optimization system using Multi-Armed Bandits to dynamically select frames for billions of uncurated short-form videos, deployed at major platform scale with low-latency serving…

#Semiconductors #GPUs #AIInfrastructure
https://arxiv.org/abs/2610.04931

SNACK stores and computes only non-zero values via a CUDA kernel, avoiding binary masks, with reported kernel-level speedups up to 7x versus a masked dense baseline on GPU.

#AIInfrastructure #GPUs #SparseTraining #DeepLearning
https://arxiv.org/abs/2610.04093

SageMaker HyperPod administration through Unified Studio gives platform teams a layered approach to cluster governance, access…

#AIInfrastructure #SageMaker #GPUs #MLOps
https://aws.amazon.com/blogs/machine-learning/best-practices-for-amazon-sagemaker-hyperpod-administration-and-governance/

A new arXiv study examines how well coding agents can prune logging code, introducing the LogRem dataset of 387 Python and Java cases. Researchers find logging removal has been underexplored compared to logging generation or…

#AIInfrastructure #Semiconductors #GPUs
https://arxiv.org/abs/2610.04716

#GPUS Hyperscale Data Held Approximately $56.4 Million of Bitcoin, Cash and Restricted Cash as of October 4, 2026

https://www.stocktitan.net/news/GPUS/hyperscale-data-held-approximately-56-4-million-of-bitcoin-cash-and-617nj3ebwufm.html?utm_source=stocktitan-bluesky&utm_medium=social

New arXiv work analyzes compute-communication trade-offs in tensor, pipeline, and hybrid parallelism for LLM inference, highlighting how prefill and decoding phases each demand distinct strategies across multi-GPU setups.…

#AIInfrastructure #LLMInference #GPUs
https://arxiv.org/abs/2610.05305

Learning-to-rank helps pick faster tensor network contraction plans for GPU-based quantum circuit simulation by scoring plans from past GPU measurements before execution.

#QuantumComputing #GPUs #Tensors #ML
https://arxiv.org/abs/2608.05819

New arXiv work improves LLM decoding on memory-constrained GPUs by replacing binary activation sparsity with a three-option choice for handling offloaded weights, aiming to balance transfer reduction and model quality.

#AI #GPUs #LLMs
https://arxiv.org/abs/2610.02598

NVIDIA describes AI factories as megawatt-scale builds costing about $60 million per megawatt, with ROI driven by earning capacity, durability, and fungibility. Operators commit such…

#AIInfrastructure #DataCenters #GPUs #ROI
https://blogs.nvidia.com/blog/productive-durable-fungible-ai-factories/

New work introduces TRACE, an agentic framework for designing heuristics that tackle dynamic resource assignment in real-world computing systems where industrial scheduling still depends on hand-written rules. By…

#AIInfrastructure #GPUs #Semiconductors #Scheduling
https://arxiv.org/abs/2610.01887