Targeting cholesterol-dependent Piezo1 activation impairs amoeboid migration in melanoma cells
PNASProceedings of the National Academy of Sciences, Volume 123, Issue 37, September 2026.
SignificanceThis study builds on a phenotypic drug screen that identified statins as inhibitors of bleb-based migration, a key mode of cancer cell movement through confined spaces. We show that statins reduce membrane cholesterol, disrupting the function ...
SignificanceThis study builds on a phenotypic drug screen that identified statins as inhibitors of bleb-based migration, a key mode of cancer cell movement through confined spaces. We show that statins reduce membrane cholesterol, disrupting the function ...
FedGuide: Diffusion Prior Alignment and Value Baseline Guidance for Heterogeneous Federated Reinforcement Learning
arXiv cs.LGarXiv:2609.18964v1 Announce Type: new
Abstract: Federated Reinforcement Learning (FRL) enables collaborative policy learning across distributed agents with heterogeneous environments. While recent methods based on variance reduction, divergence penalization, and momentum optimization improve FRL under heterogeneous settings, they still primarily synchronize policy or value-network parameters and do not explicitly address distributional mismatch among heterogeneous clients. Therefore, we propose \textbf{FedGuide}, a FRL framework that uses diffusion priors as behavior models to provide personalized data supported distributions for heterogeneous local policy learning. Instead of directly averaging local policies, FedGuide aggregates those diffusion priors through Optimal-Transport Mixture-of-Experts (OT-MoE), preserving heterogeneous behavior modes in distribution space. It further develops a Distribution Correction Estimation (DICE) value baseline to provide low-variance, return-aware guidance for local policy improvement. Experiments across heterogeneous environments show that FedGuide outperforms representative FRL methods in client-average returns, final-round performance, and worst-round robustness, while maintaining stable learning under stronger heterogeneity.
Beyond the Previous Layer: Residual Predictive Structure in Sparse MoE Routing
arXiv cs.LGarXiv:2609.17940v1 Announce Type: new
Abstract: Sparse mixture-of-experts models route each token through a sequence of expert selections. We ask whether the immediately preceding selection adequately summarizes this trajectory for predicting the next router. Using frozen OLMoE and JetMoE models, we measure the held-out predictive gain from earlier expert selections while retaining the most recent selection as a common baseline. In OLMoE, extending the history from one to eleven layers raises router-logit $R^2$ from 0.59879 to 0.66544. A preregistered JetMoE replication yields four-layer gains of 0.14275 and 0.20528 at two target depths, with paired bootstrap intervals above zero. These gains survive nonlinear decoding: adding history to a small multilayer perceptron improves $R^2$ by 0.17137 and 0.21861, whereas nonlinear decoding of the recent state alone adds 0.00139 and 0.00936 over a linear probe. Parameter-matched controls preserve the advantage, and cross-fitted history residuals predict target residuals with $R^2$ of 0.20549 and 0.23556. These findings identify residual predictive structure in expert-selection trajectories beyond adjacent-layer persistence.
Colla-Q: Toward Collaborative Experts in MoE Quantization via Minimax Precision Balancing
arXiv cs.CLarXiv:2609.18131v1 Announce Type: cross
Abstract: In this paper, we present a Mixture-of-Experts (MoE) quantization method based on activation entropy. Although quantization reduces memory and computational costs, it can substantially degrade performance. In particular, performance decline is pronounced in quantized MoE models, where individual experts have a small number of parameters that are sensitive to low-bit representation. Considering that MoE operates as an ensemble model with collaborative contributions from routed experts, a significant performance decline of a particular expert due to quantization can harm model performance. Therefore, we propose Colla-Q, a bit-allocation framework to maintain balanced performance across experts through an activation-entropy-based bit-width allocation algorithm. This approach encourages each expert to operate collaboratively in the quantized model, thereby 1) improving the overall MoE performance and 2) reducing the dependence on the calibration dataset. Since uniformly adjusting each expert's performance facilitates robustness and stability of the MoE model, the proposed MoE quantization method can generalize more consistently across different calibration datasets. Our code is available at: https://github.com/mmai-laboratory/Colla_Q
T-SANDHI: Tone Sandhi-aware Adaptive Network with Decoupled Hybrid Injection for Low-resource Taiwanese Hokkien Speech Recognition
arXiv cs.CLarXiv:2609.18194v1 Announce Type: new
Abstract: In Taiwanese Hokkien automatic speech recognition (ASR), prior studies often treat tone sandhi as a major challenge under the assumption that models fail to process implicit phonological variations. However, our experiments on Taiwanese Hokkien reveal that speech foundation models actually handle tone sandhi variations effectively, and the real performance bottleneck stems from a localized confusion between these variations and retained citation tones. To address this, we propose T-SANDHI to explicitly decouple surface acoustics from underlying lexical intent on top of a frozen Whisper backbone. Using a lexicon-guided multi-task learning structure driven by text-derived pseudo labels, our lightweight hybrid injection module integrates independent citation and sandhi phonetic streams via dynamic gating. Extensive evaluation on the TAT-MOE corpus and two blind test sets demonstrates that this explicit disentanglement effectively resolves tonal mapping confusion, outperforming baselines with strict parameter efficiency.
Higher-order pruning of experts in mixture-of-experts language models
arXiv cs.AIarXiv:2609.18916v1 Announce Type: cross
Abstract: Mixture-of-Experts (MoE) language models suffer from large parameter counts, which create a significant memory bottleneck. Expert pruning is the most direct approach for reducing this parameter count, yet existing methods make pruning decisions for each expert independently, and assume experts' contributions are purely additive. In reality, expert usage in MoEs is inherently cooperative. We derive HOPE (Higher-Order Pruning of Experts), a second-order pruning objective which provably minimizes an upper bound on the error resulting from pruning. We show that REAP (a state-of-the-art first-order pruning method) is a special case of HOPE where interaction terms are ignored. Across three frontier MoE models (up to 122B parameters), two distinct calibration sets, and multiple benchmarks (including math, instruction following, coding, and an agentic suite), we demonstrate that HOPE produces better pruning decisions than existing methods, and its advantage is most pronounced at high pruning rates and on challenging agentic workloads. At 50% pruning, HOPE outperforms all baselines and achieves an average rank of 1.58 out of 5 methods (versus 2.42 for the next-best method, REAP), with gains of up to +6.1% on agentic coding. Over all conditions, HOPE again achieves the best average rank and surpasses every other method in the majority of head-to-head comparisons. By preserving cooperative expert structure that first-order methods ignore, HOPE enables aggressive compression with minimal degradation, particularly on complex tasks where diverse expert combinations are invoked over long sequences.
Generalist-Specialist Mixture-of-Experts for Rare Pathology Detection in Multimodal Imaging
arXiv cs.AIarXiv:2609.18688v1 Announce Type: cross
Abstract: AI models for multimodal medical imaging must balance modality-specific specialization with cross-modal shared representations, a trade-off that pure Mixture-of-Experts (MoE) architectures currently fail to satisfy. Expert-based routing improves in-domain learning but may sacrifice cross-modal signals, which appear particularly important for rare (low-prevalence) pathologies in our experiments. To resolve this, we introduce Generalist-Specialist-MoE (GS-MoE), a two-branch (MoE) architecture that couples a cross-modal generalist model with distinct modality-specific specialists (experts) via domain-constrained feature fusion. On RadImageNet (1.35M images, 165 pathologies, three modalities), GS-MoE recovers detection of six low-prevalence pathologies on which every baseline scores F1 $=$ 0, with per-class gains up to +0.60 F1. It attains this while even slightly exceeding dense and specialist-only MoE aggregate baselines (MCC 0.770), while using ${\sim}53\%$ fewer active parameters at inference than the strongest investigated dense model.
MoRE: Mixture of Reused Experts
arXiv cs.AIarXiv:2609.18176v1 Announce Type: cross
Abstract: Mixture-of-Experts (MoE) architectures decouple model capacity from computational cost, yet incur high memory footprints as parameters grow linearly with the number of experts. Recurrent Transformers achieve parameter efficiency by reusing layer weights, but typically lack the capacity for competitive language modeling. We propose Mixture of Reused Experts (MoRE), a hybrid that shares expert pools across groups of adjacent layers. Each layer retains its own router but selects from a larger shared pool, expanding the diversity of routing combinations without additional parameters. To enable shared experts to distinguish between layers, we introduce lightweight learnable depth embeddings that condition each layer's input before routing. Experiments across three model scales (114M-1.15B parameters) show that MoRE consistently achieves lower perplexity and stronger downstream performance than standard MoEs and state-of-the-art weight-sharing architectures at matched compute and parameter budgets, with only minimal modifications to existing MoE implementations.
Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data
arXiv cs.AIarXiv:2609.18842v1 Announce Type: new
Abstract: The scaling laws hold that a language model grows more capable with more parameters and more training data, and Mixture-of-Experts (MoE) architectures have ridden these laws to remarkable results, activating only a fraction of an enormous stored parameter bank for each token. That success is built on static pretraining data. A deployed model faces a different world, where much of the data that would make it more useful is not in its training set but in the live interaction it is currently handling, such as the facts a user supplies or the corrections they give. A conventional model cannot learn from this data, because its weights are frozen after training. Instead, the knowledge and behaviour supplied at run time are placed in the prompt, by retrieval or instruction, and re-read on every request only to be discarded once the request ends. We ask how an architecture could learn from live interaction by writing it into its weights. Taking inspiration from MoE, we propose the \textbf{Infinite-Parameter LLM}. A compact hypernetwork turns the data given at run time into a low-rank modulation of a shared base network, so the feed-forward weights are generated from live data rather than stored in a fixed bank. Where prior weight generators read the context once and freeze, we carry a Bayesian belief over the generator's latent code and update it online, so the effective weight is re-derived from that evolving belief as the session proceeds rather than fixed after one read. The stored footprint stays fixed, yet the weights the model can compile are effectively infinite. For the knowledge and behaviour supplied at run time, carrying them in the weights rather than the prompt is amortized in compute, frees the context window, persists across turns, and can generalise better than in-context use. We specify an evaluation protocol that tests exactly this against in-context learning and retrieval.
The Other Half of the Memory Wall: Serving 35B MoEs from SSD with Trained Routing Prediction
arXiv cs.AIarXiv:2609.18063v1 Announce Type: new
Abstract: Mixture-of-experts (MoE) inference on consumer hardware is bounded by weight memory: a 35B-class model is 19.5GB at 4-bit, and sparsity shrinks the compute per token, not the bytes that must be held. Naive offloading to SSD does not help on its own, because layer N+1's experts must be chosen before layer N's output exists, so the reads cannot start early enough to hide behind compute. We present Edge0, a streaming MoE inference engine that closes the gap with a prerouter: a per-layer head predicts the next layer's routing one token ahead, and the prediction is consumed as the routing itself, so the staged expert set equals the routed set and nothing is dropped. An unmerged recovery LoRA, trained on the student path, pays back the quality lost to int4 quantization and routing replacement. On a single 24GB machine, Edge0
serves a 35B MoE at 20tok/s inside 3GiB of peak active memory, within a few points of its fp16 teacher on average across five public benchmarks. An 8B tier runs on the same framework, and the framework, checkpoints, and adapters are open source.
National Guacamole Day 2026 deals: Score free food from Qdoba, Del Taco, Tabanero, and more
Mashable
It feels like there's a new food celebration every week. We're not complaining. We didn't celebrate National French Fry Day and National Hot Dog Day, but we know that a lot of people do. And smart people celebrate by taking advantage of promotions offered by leading retailers during these events.
National Guacamole Day is no different. We can't say we had this date circled on our calendar, but many popular chains are offering limited-time deals and genuine giveaways to mark the occasion. This is where you can capitalize.
Sept. 16 is National Guacamole Day. We're seeing special offers go live at Qdoba, Del Taco, Tijuana Flats, and more. We've lined up our favorites below so you can eat for free (or at a heavily reduced rate) today.
Bubbakoo’s Burritos
Free chips and a 4-ounce guac with any entree purchase.
California Tortilla
Free guac and chips with any entree purchase.
Cocina 214
Score discounted guacamole all day on Sept. 16.
Del Taco
Rewards members can score an Epic Fresh Guacamole Burrito for $5 (online or in app).
Fuzzy’s Taco Shop
Free chips and guacamole with an in-app purchase of $5 or more with code GUACDAY.
Guzman y Gomez
Get free guacamole added to your main menu item.
Moe’s Southwest Grill
Score free guac with any entree purchase.
Moxies
Get fresh Smashed Guacamole with Tajín-seasoned chips for $12 during Happy Hour.
Qdoba Mexican Eats
Score free regular chips and guac when you make any entree purchase and enter code GUACDAY (online or in app).
Tabanero
Save 20% on your online order when you enter promo code GUAC20 during checkout.
Tocaya Modern Mexican
Rewards members can get a free half-size chips and guac with a purchase of $15 or more.
OPPO ColorOS 17 发布：全新“流体设计”、新一代小布
IT之家
IT之家 9 月 17 日消息，在今日的 OPPO ColorOS 17 发布暨开发者大会上，ColorOS 17 正式发布，口号为“超流畅，更懂你”。
ColorOS 17 的流畅双引擎迎来全面升级：极光引擎从绘制层深入渲染层，实现融合渲染，内存占用和渲染负载降低；潮汐引擎通过个性化行为感知链实现精准调度，带来行业首个记忆后台。
ColorOS 17 采用“流体设计”，带来凝光视效、流体动效、柔性反馈，比如拖动音乐播放器进度条，会有光影变化。
性能优化方面，ColorOS 17 带来个性化感知调度，官方称之为“记忆后台”，前台应用性能前倾、重要后台应用精准保活、非重要后台智能冷存，后台稳定保活率提升 55.6%。
此外，相册选图、小程序启动、应用内跳转等高频场景均实现秒加载、秒启动、秒跳转。
ColorOS 17 是迈向个性化主动智能的关键一步，On-Device Compute 端侧大模型、Persona X 记忆共生引擎、Agent Matrix 智能体生态框架，三大 AI 技术底座也迎来全面突破。
去年，OPPO 行业首创 AI 一键闪记，率先将个性化记忆体验带给用户，其首创的取餐码上线以来累计使用超 10 亿次，一年内已成系统标配。
今年，ColorOS 17 一键闪记在原有记码记账记视频，记大师参数的基础上，新增记取件码、记联系人信息、记演唱会抢票时间等场景。一键闪记可在任意界面结合当前内容给出意图推荐。
新一代小布支持更长的上下文对话，智能化能力也迎来升级；用户可自定义小布的情绪、表达风格、回复长度等。
目前，小布建议一站式主动生活服务已与 40+ 头部三方合作，覆盖 150+ 服务种类、700+ 服务场景。
小布来电功能可帮助用户避免错过重要行程，比如打车目的地和订票出发点不一致，小布会强提醒用户进行检查。
ColorOS 17 还联合地图 App 带来流体云融合导航功能，可在流体云通知部分显示导航指引，依托自研“蜂群感知技术”，实现微观区域高精度感知，播报准确率达 95%。
此外，用户坐地铁时，流体云融合导航会显示每个城市地铁口的图案（夜间地铁口会亮灯），以及附近的地标建筑图案。
在影像上，ColorOS 17 带来全新“AI 修图师”，自研达芬奇视觉理解大模型，通过六大专家模型保证照片真实自然。同时，它会基于图片的理解主动推荐修图建议，还能根据参考图进行复刻。
ColorOS 17 推出“AI 笔记”，打造一站式个人知识管理空间。支持写作、录音、拍照扫描、视频链接导入、文件文档导入、小布记忆导入等丰富记录方式；用户还能通过 AI 笔记助手，用自然语言随时调整内容，同步生成结构化信息速览图，还能
Unifying Semantic Priors and High-Frequency Traces: Enhancing V-JEPA with Mixture-of-Experts for Robust Synthetic Image Forensics
arXiv cs.CVarXiv:2609.16778v1 Announce Type: new
Abstract: The unchecked proliferation of manipulated images on social media platforms has increased the spread of misinformation, posing a severe threat to public trust and information integrity. Modern deepfake detectors typically rely on Vision Transformers (ViTs) to capture the low-level inconsistencies that characterize fully synthetic or locally tampered images. However, the global understanding of such foundation models is not enough to discriminate alone between real and fake multimedia content, especially in challenging scenarios where images are compressed or transmitted through social media. In this paper we pioneer the application of Joint-Embedding Predictive Architecture (JEPA) models to deepfake detection, taking advantage of the generalized representation of visual reality that such World Models have exhibited. We hypothesize, and empirically demonstrate, that the intrinsic world understanding of JEPA models can be used as a strong prior for a deepfake detector. To fully exploit JEPA capabilities, we propose MoE-JEPA, a dual-stream architecture for deepfake detection. By enhancing a V-JEPA 2 backbone with a Residual Mixture-of-Experts (MoE) mechanism, along with a noise stream branch, our model dynamically internalizes forensic knowledge. Furthermore, a Gated Attention Multiple Instance Learning (MIL) module is employed to ensure precise spatial semantic understanding. Evaluated on the SID-Set benchmark, comprising 300K AI-generated, tampered and authentic images, MoE-JEPA establishes a new state-of-the-art with an accuracy of 95.54%, successfully outperforming vastly larger models.
Which Pretext Task Transfers? Self-Supervised Pretraining Objectives for Lung Ultrasound
arXiv cs.CVarXiv:2609.16551v1 Announce Type: new
Abstract: Self-supervised learning (SSL) can reduce the need for labelled medical images, but the choice of pretext objective remains unclear for lung ultrasound (LUS). Contrastive learning, masked reconstruction, and joint-embedding predictive architectures (JEPA) differ in the space in which their targets are defined, yet existing ultrasound studies compare them under different corpora, backbones, and evaluation protocols. We compare these three objective families using the same encoder backbone, pretraining corpus, optimisation schedule, and frozen-evaluation protocol. Encoders are pretrained on COVID-BLUeS LUS videos and evaluated with linear, $k$NN, and attentive probes at 5\%, 10\%, 50\%, and 100\% label budgets. Evaluation is performed on POCUS using patient-level five-fold cross-validation and on the independently acquired Mendeley-Uganda dataset, which is excluded from both pretraining and probe fitting. At the full label budget under linear probing, VideoMAE and V-JEPA achieve $66.5 \pm 13.1$ and $65.4 \pm 11.7$ balanced accuracy on POCUS, while MoCo achieves $42.1 \pm 1.2$. On Mendeley-Uganda, the ranking reverses: MoCo performs best at $62.7 \pm 1.0$, followed by VideoMAE at $53.8 \pm 2.8$, while V-JEPA falls near chance at $35.1 \pm 4.9$. These results show that POCUS probe accuracy alone does not identify the objective that transfers best across datasets. We also outline planned representation-level analyses to examine this reversal. Code is publicly available at https://github.com/moeinheidari7829/LUSVideoSSL.
What Breaks Under Pruning in Smart Homes, and When? Evaluating LLM Degradation Across Architectures and Task Complexity
arXiv cs.CLarXiv:2609.17515v1 Announce Type: new
Abstract: Pruning can reduce the deployment cost of large language models (LLMs), but its impact on context-grounded tool calling remains poorly understood. We systematically study pruning-induced degradation in smart-home tool calling across four LLMs spanning dense Transformer, dense hybrid, and mixture-of-experts (MoE) architectures, together with depth, width, hybrid, and expert pruning methods. After post-pruning supervised fine-tuning (SFT), we evaluate more than 19,500 instances from three smart-home datasets. Beyond aggregate task accuracy, we characterize degradation along two dimensions: action components (i.e., operation, device, argument, and value) and task complexity. Our results show that dense models have narrow safe pruning regions followed by sharp degradation, while MoE models tolerate substantially more pruning. Pruning degrades grounded specificity before schema-level intent, and aggressive dense pruning can induce systematic over-refusal. These findings highlight the importance of evaluating pruning beyond aggregate accuracy when selecting pruned LLMs for reliable tool execution.
PopSockets expands popular Low-Pro line with new 3-in-1 iPhone cases
Mashable
PopSockets just launched the Low-Pro Case, a new line of MagSafe iPhone cases and the first expansion of its Low-Pro line. The three-in-one MagSafe case supports wireless charging and also includes a collapsible Low-Pro Grip and a built-in phone stand.
Earlier this year (June 16, to be exact), PopSockets launched its Low-Pro Grip — a series of ultra-thin magnetic grips designed to lie flush against the back of your phone — exclusively in Apple stores. The Low-Pro Grip is just 2.6 mm when collapsed, and PopSockets says the launch received tons of positive feedback (most likely from people like me who can't stand when things are even a little bit off). PopSockets founder David Barnet told Mashable the Low-Pro is the brand's most popular new launch since the company's earliest days.
So, naturally, the company is taking the concept further by embedding that same disappearing grip directly into a protective phone case. According to PopSockets, the new Low-Pro Case offers:
Integrated Low-Pro Grip
Adjustable stand for both portrait and landscape viewing
10-foot drop and 360-degree phone protection
Optimized for MagSafe cases
Wireless charging (meaning the phone charges wirelessly through the case and grip)
Here's what the new case looks like in practice:
The new PopSockets Low-Pro Case features a built-in grip and integrated kickstand ring.
Credit: Mashable | Timothy Beck Werth
A side view of the PopSockets Low-Pro Case.
Credit: Mashable | Timothy Beck Werth
And here's how it works: instead of snapping an accessory onto your existing phone case, this is a grip and case in one. It uses a flexible unibody design with a built-in grip that expands when you need to hold your phone, then collapses flat when you slide it into your pocket. It also has a metal ring that functions as a phone stand.
Launching Sept. 15 for $59.99, the new PopSockets Low-Pro Case is available for the iPhone 17 Pro and Pro Max as well as the newly announced iPhone 18 Pro and iPhone 18 Pro Max (the Low-Pro Grip costs $39.99 on its own). For now, it comes in black and burgundy and is available for sale on Amazon, Best Buy, and the PopSockets website.
Our tech editor demoed the new case in person with Barnett and can confirm that the grip is "extraordinarily thin." While it might feel a bit flimsy due to the ultra-thin, flexible parts, Barnett says they tested the grip and found it could support over 40 pounds. The company discovered this fact in a very scientific "Fanta test," as you can see in this TikTok:
AI networking startups race to replace Nvidia's NVLink
The RegisterAs Nvidia expands its influence through its NVLink Fusion tech, rival networking vendors are scrambling to bring alternative interconnects and switches to market. At the AI Infra Summit this week, Delos Data and Cornelis Networks officially entered the scale up networking race. Scale-up fabrics, like NVLink, are what have allowed Nvidia to make eight, 72, and now 576 GPUs behave as one enormous AI accelerator. To catch up, rivals like AMD have embraced emerging protocols like Ultra Accelerator Link. Today, these protocols are largely being tunneled over standard Ethernet switches. For instance, AMD is using Broadcom’s 102.4 Tbps Tomahawk 6-based switches connecting to custom I/O dies on the MI455X. Purpose-built UALink switches and physical interconnects remain elusive, but that won’t be the case for long if Cornelis and Delos have their way. The two companies are approaching this challenge from a few different angles, including standardization, software optimization, and physical hardware. Setting the standard for the Never-Nvidia network At AI Infra on Monday, HPC-centric networking vendor Cornelis introduced the Active Compute Fabric (ACF), which seeks to establish an open architecture for scale up and scale out networking that integrates programmable compute into the fabric. The standard signals Cornelis’ entry into the scale up networking arena. Spun out of Intel in 2020, Cornelis’ Omni-Path tech was originally designed as a scale-out interconnect for high-performance computing applications, including supercomputers like Trinity and Lynx. With the imminent launch of the company’s 800 Gbps-capable CN6000-series switches and NICs, the company has its sights set not only on bringing its tech to a broader audience through the open Ultra Ethernet protocol, but also on scale-up networking. ACF expands on the mission of technologies like UALink and the Ethernet for Scale-Up Networking, another scale up networking protocol, to set a baseline for in-network compute capabilities across a wide range of hardware, not just Cornelis’ own CN-series parts. One of the key technologies behind Nvidia’s NVSwitch ASICs is support for SHARP, which allows for things like in-network collectives to be offloaded to the switch ASICs, freeing up GPU compute and cutting down on communication overheads. Collective acceleration isn't new by any means, with major networking vendors from Broadcom to Cisco having implemented it in some capacity. As an open architecture, ACF appears to set a least common denominator so that customers deploying these systems know exactly what they’re getting. However, it doesn’t stop there. Cornelis sees opportunities to accelerate a variety of other functions. We’ve detailed a few below: KV cache offloading used to store and retrieve model state across multiple sessions. MoE expert dispatch to reduce duplicate transfers and communication overhead for mixture-of-experts models during inference and training. Message passing interface offload for HPC-centric applications. In fabric checkpointing for failure recovery during training and other large workloads. “Communications overhead and synchronization can leave expensive accelerators underutilized,” the company explained. These in-network accelerators “allow the network to operate on data as it moves through the system.” According to Cornelis, the savings potential from reclaiming that idle compute is significant. “In a 100,000 GPU system, Cornelis modeling of public data shows that roughly half of all GPU hours are spent waiting for data, worth about $1.68 billion a year in wasted capacity and 500 GWh of power.” As usual, take these claims with a grain of salt. However, reclaiming GPU idle time would equate to significant savings. Eliminating all the bottlenecks that contribute to them is easier said than done. A new kind of NIC for the AI age While Cornelis champions its ACF architecture, Delos Data, a startup founded by former Barefoot Networks and Intel execs, aims to tackle the physical layer with a series of new network reference designs. Marketed under its Nonstop AI portfolio, the network interfaces span the full gamut of connectivity from co-packaged interconnects to more traditional NICs. The idea, the company explains, is to provide customers with a system-level blueprint for speeding up data movement between endpoints and enabling larger compute domains scaling beyond a single rack. Delos’ data interface will be offered in three form factors. The first is an I/O die capable of more than 30 Tbps of aggregate bandwidth or about 8 TB/s in either direction. That’s more than double the interconnect bandwidth of either Nvidia's or AMD’s latest accelerators, which cap out at 3.6 TB/s. Just how competitive that ends up being will depend on when Delos’ I/O chiplets actually see deployment. That timeline is going to depend heavily on integration since those I/O dies need to be integrated directly into the accelerator package, which requires a high-degree of co-design. Critically, Delos isn't trying to trap its customers in a walled garden to the same extent that Nvidia does with its NVLink Fusion I/O dies or IP. At least as of writing, NVLink Fusion still requires customers to buy NVSwitches for scale up networking. Delos’ chiplets are protocol agnostic. They don’t care whether you’re using ESUN, UALink, or something else. Chip designers that'd rather focus their efforts and capital on the AI bits of their accelerators – which, it turns out, applies to most hyperscalers – could simply license Delos' chiplet design. This protocol agnosticism also extends to Delos’ near-packaged optics (NPO) tech, which will integrate a 10-plus Tbps data interface — that’s 2.5 TB/s bidirectional bandwidth in Nvidia speak — with optics engines from leading optics suppliers. Rack scale systems, like Nvidia’s NVL72, have largely relied on copper interconnects up to this point due to power constraints. As systems grow from 72 GPU rack systems to row-scale clusters with 576 and eventually 1,152 GPUs, optics become unavoidable. While integrating optics directly into the accelerators is possible using tech available today, the blast radius of a failed optical module is considerable. NPO offers an alternative. Copper interconnects are used within the rack, with optics provided via user-serviceable NPO modules between racks. Finally, Delos is working on a 400-plus Gbps NIC, which like the rest of its data interface offerings is protocol agnostic. This is arguably the least surprising entry in the entire lineup. Scale out networks are still going to be required for large training clusters as well as for front-end access and storage networks. These interfaces build on Delos’ existing compute reference design, which we looked at during Computex, as well as its Nonstop AI software platform, which is designed to facilitate the configuration and monitoring of these switched fabrics or meshes in order to enable dynamic rerouting of traffic in the event of a link failure. Telemetry gathered by its data interface offerings allows for even greater visibility into the network, allowing for faster rerouting and recovery. Fueling competition Investors appear more than eager for more competition in the scale up arena. Alongside its product announcements, Cornelis announced approximately $205 million in funding to bring its next generation of scale up and scale out networking products to market. Meanwhile, Delos Data announced Tuesday that it’s now raised more than $100 million in funding with support from venture capital firms Matrix, Playground, and Socratic Partners among others. ®
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