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https://www.together.ai/blog/expanding-our-enterprise-inference-capacity-with-ibm-cloud-and-nvidia

Together AI announced that it is expanding its enterprise‑grade inference capabilities by partnering with IBM Cloud and NVIDIA. The move leverages IBM’s cloud platform for scalable compute resources w

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Hardware

Hybrid Latent Attention for Looped Language Models

DGX agent

arXiv:2610.07940v1 Announce Type: cross Abstract: Looped language models apply the same stack of layers T times to each token, which deepens the model without adding parameters but multiplies its key-

hardwarearxiv-cs-ai

7 Oct 2026

'It's well worth spending the money on tokens if the business is getting the benefit.' — IBM Cloud GM Alan Peacock We're scaling open-source…

'It's well worth spending the money on tokens if the business is getting the benefit.' — IBM Cloud GM Alan Peacock We're scaling open-source inference with @IBM + @nvidia on a dedicated B300 cluster o

hardwaretogether-ai--x

Mecka, which collects human motion data to train humanoid robots, raised a $60M Series B led by Sequoia, with participation from Nvidia, M12, and others (Marina Temkin/TechCrunch)

Marina Temkin / TechCrunch: Mecka, which collects human motion data to train humanoid robots, raised a $60M Series B led by Sequoia, with participation from Nvidia, M12, and others — Mecka AI, a start

hardwaretechmeme

Our SynthID technology lets you easily verify if an image, video, or audio file is AI-generated, and today we’re expanding access. Now anyon…

Our SynthID technology lets you easily verify if an image, video, or audio file is AI-generated, and today we’re expanding access. Now anyone can go to http://synthid.com to check files for a SynthID

hardwaregoogle-ai--x

Scaling Decision Optimization to 100 Million Variables and Beyond with mPDLP in NVIDIA cuOpt

NVIDIA cuOpt’s Multi‑GPU Primal‑Dual Hybrid Gradient (mPDLP) solver distributes linear programming problems across NVLink‑connected GPUs, cutting per‑GPU peak memory by up to six times and handling up

ScanSTL: Parallel Robustness Evaluation for Signal Temporal Logic

arXiv:2610.07346v1 Announce Type: new Abstract: Repeated evaluation and differentiation of Signal Temporal Logic (STL) robustness can become a computational bottleneck in robot planning and control. S

hardwarearxiv-cs-ro

Source: Nvidia considered a last-minute OpenRouter bid, telling its leadership it was prepared to make a generous offer, but OpenRouter didn't want to wait (The Information)

The Information: Source: Nvidia considered a last-minute OpenRouter bid, telling its leadership it was prepared to make a generous offer, but OpenRouter didn't want to wait — This July, Nvidia caught

hardwaretechmeme

T-CCL: Resource Efficient and Performant Collective Communication using Tensor Memory Accelerator

arXiv:2610.07098v1 Announce Type: cross Abstract: Large transformer-based models increasingly depend on multi-GPU execution, which requires frequent collective communication among GPUs. Existing commu

The Machines that Make the Machines

NVIDIA’s Seattle Robotics Lab and Isaac engineering team created reusable robotic systems to automate GB300 tester‑tray assembly, achieving over 95 % success on busbar tasks with a 160‑second cycle ti

Model Releases

The Windows launch event today has mentioned 'local AI' 41 times (so far) 🤯

model-releasesgeorgi-gerganov--x

The Windows launch event today has mentioned 'local AI' 41 times (so far) 🤯 Media It is very cool to see llama.cpp on the big stage in todays Windows event! The software and hardware stacks are finall

Trained a ~20K LM (probably smallest) that can still write stories

I’ve been pushing TinyStories-style models downward in size, and this is the smallest one so far: MacroStories — 19,969 parameters, 81 KB FP32 https://huggingface.co/raincandy-u/MacroStories For scale

hardwarer-localllama

VisionWeave: Weaving Elastic Visual Representations as a Native Capability of MLLMs

arXiv:2610.07987v1 Announce Type: cross Abstract: Multimodal large language models have become the dominant paradigm for visual understanding, but incur substantial costs by encoding inputs into dense

Wikidata Search Traces: A Dataset for Training Knowledge Graph Search Agents

arXiv:2610.06650v2 Announce Type: replace Abstract: Wikidata is one of the largest open knowledge bases, yet answering a complex question over it still requires a SPARQL query that names the right ent

hardwarearxiv-cs-cl

@WSJ @nvidia @M12vc @SamsungNext @robotventures @usv @ycombinator @MenloVentures Join us at NousCon to celebrate and perhaps catch some snea…

@WSJ @nvidia @M12vc @SamsungNext @robotventures @usv @ycombinator @MenloVentures Join us at NousCon to celebrate and perhaps catch some sneak peeks https://x.com/NousResearch/status/210504377770675421

hardwarenous-research--x

AffordCraft: Scalable Construction of Task-Ready Simulation Assets from Single Images

arXiv:2610.06643v1 Announce Type: cross Abstract: Robot learning in simulation depends on the objects the simulator offers. Many tasks need objects with separate parts, joints that allow the required

Answer Set Networks: Casting Answer Set Programming into Deep Learning

arXiv:2412.14814v2 Announce Type: replace-cross Abstract: Although Answer Set Programming (ASP) allows constraining neural-symbolic (NeSy) systems, its employment is hindered by the prohibitive costs

Model Releases

[Benchmark] Running Local LLMs on Orange Pi 5 Plus (RK3588, 16GB): Ollama Tok/s, NPU Offloading, Core Pinning & Thermals

model-releasesr-localllama

Disclosure: This unit was provided free of charge by Orange Pi for testing. No editorial review, no preconditions, no script. All data, bottlenecks, and thermal behavior are reported directly from har

Control How Your GPU Shares Work with Green Contexts

In CUDA 13.1 “green contexts” let applications explicitly partition a GPU’s execution resources—such as Streaming Multiprocessors (SMs) and work‑queue slots—within one process. By assigning dedicated

CoreWeave targets GPU utilization in continuous AI post-training

GPU utilization during post-training depends partly on how efficiently infrastructure moves data and loads updated models. As enterprises continually refine AI agents, reducing delays between training

Dell’s AI Data Platform gets a knowledge graph for agents and faster Nvidia processing

Dell Technologies Inc. today expanded the Dell AI Data Platform with a semantic layer and a knowledge graph intended to give artificial intelligence agents trusted context from a company’s own data. D

Expanding our enterprise inference capacity with IBM Cloud and NVIDIA

Together AI has partnered with IBM and NVIDIA to launch the first dedicated, large‑scale AI inference cluster on IBM Cloud, consisting of NVIDIA B300 GPUs and Spectrum‑X Ethernet networking engineered

hardwaretogether-ai-blog

FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing

arXiv:2508.12021v2 Announce Type: replace Abstract: Unsupervised federated learning (UFL) enables privacy-preserving distributed training without data labeling, yet practical deployment remains challe

Frozen Factor or Spectral Band? Disentangling Two Choices in Low-Rank LoRA

arXiv:2610.06621v1 Announce Type: new Abstract: Spectral variants of low-rank adaptation (LoRA) choose both a subspace and which factor to freeze. We separate these choices by freezing the input facto

Image-Based Breast Implant Detection for Mammography Dataset Curation and Near-Real-Time Deployment: Comparing Foundation Models and Task-Specific Convolutional Models

arXiv:2610.03817v1 Announce Type: cross Abstract: Purpose: To evaluate the performance-feasibility tradeoffs of foundation models (FMs) and task-specific convolutional neural networks (CNNs) trained f

LogSig-SSM: Time-Series Modelling with Multi-Scale Log-Signature Compression for State-Space Models

arXiv:2610.05051v1 Announce Type: new Abstract: Time-series data are often sampled irregularly at high frequencies and exhibit long-range dependencies, which makes long-horizon modelling difficult. Co

MC-Sparse: Deconstructing and Closing the Dense-Sparse Attention Gap in Diffusion Transformers

arXiv:2610.06801v1 Announce Type: new Abstract: Sparse attention is a primary approach to reducing the latency of diffusion transformers in long-sequence generation tasks, such as video and high-resol

Measuring and Reducing Cross-Vendor Mismatch in Language Models

arXiv:2610.05458v1 Announce Type: new Abstract: Running the same language model on different graphics processing unit (GPU) vendors can produce different logits, even when the model weights and inputs

MetaKernelBench: Measuring GPU Kernel Knowledge Transfer Beyond Code

arXiv:2610.05014v1 Announce Type: new Abstract: Recent GPU kernel optimization agents retain what they learn in knowledge bases or as distilled skills. Kernel benchmarks score each attempt's implement

PhysMamba: Selective State Space Models as Learned Articulated Body Simulators

arXiv:2610.04014v1 Announce Type: new Abstract: We introduce the first learned articulated body simulator based on a selective state space model (SSM), called PhysMamba. PhysMamba predicts next-frame

Reflection AI debuts open-source Beam model with 501B parameters

Reflection AI Inc. today introduced Beam, an open-source large language model with 501 billion parameters. The launch comes a few months after the startup raised funding at a 25 billion valuation. Aro

RESOLVE: Language-Agnostic Validation of GPU Kernels Through Testing, Reduction, and Proof

arXiv:2610.05683v1 Announce Type: cross Abstract: AI systems can now write and optimize production GPU kernels, but validating them remains an important challenge. Evaluating the kernel on a few rando

Rethinking Least-Core Computation in Contextual-Distractor Games

arXiv:2610.06087v1 Announce Type: new Abstract: Game-theoretic attribution explains a model by assigning credit to its features or training examples. The least core has attracted interest as an altern

Model Releases

Saying, Not Knowing: Aggressively GGUF-Quantized Small Language Models Still Write Rare Words They Can No Longer Define

model-releasesarxiv-cs-lg

arXiv:2610.04403v1 Announce Type: cross Abstract: Post-training quantization to the GGUF format's mixed-precision K-quants is commonly how open-weight language models reach consumer hardware, yet its

Scale Bitwise-Deterministic Pretraining with NVIDIA Megatron Core

Megatron‑LM PR #7262 introduces a workflow that records ordered per‑rank tensor fingerprints to pinpoint and correct nondeterminism in large‑scale pretraining. By implementing low‑overhead kernel twea

Sources: SpaceX is seeking to raise 40B, including ~10B in bank loans and ~$30B in investment-grade debt, to purchase Nvidia chips, in a deal led by Apollo (Financial Times)

Financial Times: Sources: SpaceX is seeking to raise 40B, including ~10B in bank loans and ~$30B in investment-grade debt, to purchase Nvidia chips, in a deal led by Apollo — Blockbuster debt deal is

hardwaretechmeme

Static Bootstrap Placement for Encrypted Language Model Decoding

arXiv:2610.04912v1 Announce Type: cross Abstract: Language models increasingly serve prompts that carry private data, and secure inference under homomorphic encryption lets a client outsource the comp

Super-Resolution in The Right Latent Space: A Frozen Vision-Foundation Substrate

arXiv:2610.04781v1 Announce Type: new Abstract: In an image latent space, the embeddings of high-resolution, natural, and sharp images form a manifold. Degradation of high-resolution images pushes the

Transformers vs RNNs vs SSMs: Where Does Memory Actually Live? [D]

hardwarer-machinelearning

Someone who has always loved looking at the space between different AI techniques, this time I went a little deeper into the memory trade-offs between RNNs, Transformers and SSMs. I found it interesti

Vision Transformer Ensembles for Panoramic Street Segmentation

arXiv:2610.06063v1 Announce Type: new Abstract: Semantic segmentation of street panoramas can support detailed descriptions of urban environments, yet small datasets and unequal training costs make mo

Why Telecom Operators Are Building Their AI Strategy on Open Models

Telecom operators are increasingly building their AI strategies on open models — and the reasons go beyond mere cost. Open models give telcos the ability to trust, control and customize AI across thei

hardwarenvidia-blog

All the drama around AI’s takeover of mathematics

This past year, OpenAI, Anthropic, and other labs have announced breakthroughs on numerous long-standing mathematical problems, in some cases pushing well beyond what researchers expected current syst

hardwarethe-verge-ai

Anthropic Subscriptions Offer 5x+ More Value Than OpenAI

Anthropic’s paid subscription plans deliver more than five‑times the perceived value of comparable OpenAI offerings when evaluated by cost per inference unit and overall revenue contribution. The heav

hardwaresemianalysis

Batched Speech Decisions Without Decoding: Single-Token Supervision Lets a Frozen LLM Hear Beyond the Transcript

arXiv:2610.02638v1 Announce Type: new Abstract: Full-duplex voice agents make many small, closed decisions, which current systems answer by slow autoregressive decoding. We propose DuplexJev, which fe

Congrats to the Reflection team on Beam 🎉 Be sure to join us next week at our NYC meet-up with the Reflection team and NVIDIA https://luma.…

Congrats to the Reflection team on Beam 🎉 Be sure to join us next week at our NYC meet-up with the Reflection team and NVIDIA https://luma.com/TAIxNvidiaxReflection Introducing Beam: a highly efficien

hardwaretogether-ai--x

DyRA: Dynamic Residual Approximation for Efficient Matrix Multiplication in DNNs

arXiv:2610.02882v1 Announce Type: cross Abstract: Large-scale foundation models achieve strong performance across diverse tasks, but their size makes inference costly, largely due to dense matrix mult

Efficient Memory Crystallization for Graph Learning under Non-Stationary Distribution Shifts

arXiv:2610.02795v1 Announce Type: new Abstract: Deep graph learning models deployed in real-world systems often need to cope with non-stationary environments, where the underlying graph distribution d

Exclusive: Iterate.ai’s Lifeboat runs up to six times more AI agent sessions per GPU

Enterprise artificial intelligence software company Iterate Studio Inc. today launched Lifeboat, an inference engine for large language models that has confidential computing built in. Iterate.ai says