チップ

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Before we start: I'm hosting the first-ever The Pragmatic Summit on 11 February, 2026, in San Francisco. Join 400 top engineers and leaders as we answer the question: How is AI reshaping software engineering, dev workfl…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Robotics Gets Its PyTorch Stack.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：This article walks through what each technique actually does, why skipping them costs real money and real latency, and then gets hands-on with five specific methods people are running in production right now.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Computers Aug 28, 2026 • 6 min read IFA 2026 bets on local AI, robots and thinner devices IFA 2026 runs September 4–8 in Berlin, with Xiaomi’s debut, DJI robot vacuums, local AI PCs and new smart-home hardware in focus.…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：It’s Time. We’re in the Endgame now.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.26383v1 Announce Type: new Abstract: Autonomous robots performing laboratory tasks depend on 3D reconstruction pipelines that can turn raw camera streams into actionable object representations within the latency budget of a physical control loop. Neural 3D reconstruction methods have demonstrated high-quality view synthesis, but their real-time viability across the compute platforms on which laboratory robots actually run remains poorly characterized. In this work, we present a systematic compute-platform benchmark of neural 3D reconstruction methods, evaluating NeRF and 3D Gaussian Splatting training and rendering on GPU-enabled computing devices ranging from single-board computers to server-class nodes, and place Meta's SAM3D single-image reconstruction on the same axes to quantify its latency and fidelity gap relative to per-scene optimization. Our results show that Gaussian Splatting yields higher rendering quality than NeRF at greater GPU cost, and that onboard compute is insufficient for full per-scene optimization at interactive rates. Our preliminary assessment on SAM3D indicates that it delivers plausible object geometry within seconds, but with detail mismatches that can compromise downstream manipulation. Together, these findings motivate tiered pipelines in which lightweight feed-forward reconstruction sustains the real-time perception-and-tracking loop for laboratory robots, while heavier neural reconstruction is scheduled selectively on suitable compute.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Anthropic pushes into physical world with new standard to help AI agents operate machines Skip Navigation Anthropic announced the Model Hardware Standard, or MHS, a new interface that will make it simpler for AI agents…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：At scale, your training efficiency is determined by a single metric: "goodput", the...

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：wanno: AI web & app builder Describe your idea. It's live. Only for iPhone Free · In‑App Purchases · Designed for iPhone. Not verified for macOS. Age Rating 4+ Years Category Productivity Developer FEDERICO GUILLERMO CA…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Nvidia Corp. has reportedly bought Hugging Face Inc., a startup with a popular platform for hosting open-source artificial intelligence projects. Rumors that an acquisition was in the cards first leaked on Monday. Business Insider broke the news that Hugging Face had received interest from multiple prospective buyers. On late Wednesday, The Information reported that Nvidia […] The post Nvidia reportedly acquires AI project hosting platform Hugging Face for $12.9B appeared first on SiliconANGLE.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Sopro V2 Turbo Today we are presenting a new family of TTS models called Sopro V2, and open-sourcing our fastest one: sopro-v2-turbo. Sopro V2 Turbo is a 120M-parameter voice-cloning text-to-speech model that streams, r…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Artificial intelligence is changing what storage and data management platforms look like. In collaboration with Super Micro Computer Inc. and Solidigm, DataDirect Networks Inc. has introduced DDN Enterprise AI HyperPOD, built on Nvidia Corp.’s AI Data Platform. The goal is to simplify the storage, scaling and deployment of AI inference for enterprise workloads. “Customers are […] The post The AI storage stack gets an inference-era rethink appeared first on SiliconANGLE.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Hitika Aug 17, 2026 The more I worked with AI systems, the more I noticed a familiar pattern in my own learning: exposure, feedback, mistakes, adjustment, and repetition. Over the past month, I have been interning at a…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Hearing Aid Technology Signia MaX Hearing Aids in San Mateo & San Carlos Signia MaX, the full name being Multi-adaptive Xperience, is Signia's new flagship platform, announced on August 24, 2026 and available in the Uni…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Using domestic chips shows that Chinese vendors are becoming more self-reliant and improving inference performance on AI chips.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Research August 27, 2026 For long-horizon agents, model capability alone does not determine system capability. Infrastructure orchestration multiplies what models can do. INT21’s SwarmOS tested this hypothesis on the AR…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：AMD Jumps From ROCm 7.14 To ROCm 10.0 With ROCm.AI Back in July ROCm 7.14 was announced as their new production release built atop TheRock build system and introducing Ryzen AI 400 series support. The versioning choice…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Every agent that writes code needs somewhere to run it, and no two vendors quote the same units. This comparison measures burst cold start across E2B, Daytona, Modal, Cloudflare, and Vercel, normalizes per-second rates to cost per 1,000 executions, and maps filesystem persistence, idle billing, and egress policy against primary sources verified August 27, 2026. The post Best Agent Sandboxes in 2026: Cold Start, Per-Second Pricing, and Network Policy Across E2B, Daytona, Modal, Cloudflare, and Vercel appeared first on MarkTechPost.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Take that, OpenAI! Anthropic! Chinese AI models have surpassed their U.S. counterparts in token consumption on OpenRouter. You might think U.S. AI companies dictate the AI economy. You’d be wrong. According to dat…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：You could have a mechanical duck waddling through your home before Christmas. Hugging Face‘s Pollen Robotics on Thursday opened pre-orders The post This duck will teach you reinforcement learning — and pick up your socks appeared first on The New Stack.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：On Nvidia's earnings call Wednesday, CEO Jensen Huang casually announced the company had "achieved AGI," one of the tech industry's ultimate goals some of its biggest players have spent years chasing. Almost immediately, Huang dismissed the coveted milestone as "senseless." He's right. For the supposed finish line of the AI race, there is no consensus on what artificial general intelligence means, let alone how we'll know when we've actually got there, which makes achieving it equally arbitrary. Asked about OpenAI's pursuit of AGI, Huang said that when it comes to Nvidia, "for many tasks, we could say that we've already achieved AGI." … Read the full story at The Verge.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Self-hosted speech AI carries an observability trade-off: the numbers that drive capacity planning and cost management stay locked inside the vendor container. Deepgram closes that gap on Amazon SageMaker AI with two capabilities that land billing, usage, and per-GPU metrics directly in your own Amazon CloudWatch account.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Serving automatic speech recognition (ASR) models at scale is costly when each request uses only a fraction of a GPU. Learn how NVIDIA CUDA Multi-Process Service (MPS) with NVIDIA Triton Inference Server on Amazon EC2 GPU instances cuts GPU infrastructure by 75% while holding sub-second latency at 92.1 requests per second per GPU.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：REC · YOUR AGENT IS EDITING FFmpeg as a service, built for agents. Typed operations your agent calls over MCP or REST. Trim, resize, compress, convert. A validated request in, a finished file out. No shell, ever. Start…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The Jetson Orin Nano 2 comes as the AI chip giant targets physical AI expansion.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Hugging Face's Pollen Robotics has launched its second cute AI robot, the Microduck, a one-eyed biped standing just under 10 inches tall. It's available to preorder now for $399 in cream, graphite, lavender, and sky blue, and Pollen Robotics says it plans to start shipping the little robot "before Christmas 2026." Video demos of the Microduck show it picking up socks and markers, kicking around a ball, and zipping around on tiny rollerskates. Pollen Robotics says it can also "react to its surroundings" and follow around a laser pointer, or users can control it with a game controller. The Microduck's software is open-source, like Hugging Fa … Read the full story at The Verge.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：I found my old iPhone 4 in a drawer and wondered: Does it work? Is it worth anything? What should I do with it? I went looking for answers.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The companies are expanding their collaboration beyond GPUs into CPUs, government AI infrastructure and robotics.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：NVIDIA’s Gamescom announcements are revealing what’s next for GeForce NOW, with new ways to play, more supported devices and platforms, and even more big PC games headed to the cloud. New NVIDIA DLSS 4.5 technology controls give members more ways to fine-tune gameplay, while expanded support for new Steam devices, GOG single sign-on, Firefox browser […]

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：For the complete documentation index, see llms.txt. This page is also available as Markdown. Qwen3.8-Flash-Next is a new open-weight, 125B parameter MoE multimodal model from Qwen. Built on the new Qwen4 architecture, i…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The Independent AI Coding Community AI Tools Search & browse all AI tools AI Jobs International roles · opportunities Creative Studio Image · Video · Training AI Models Curated models, explained AI Skills Handy prompts,…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Notifications You must be signed in to change notification settings Fork 0 Star 0 BranchesTags Open more actions menu Latest commit History 4 Commits 4 Commits Folders and files NameName Last commit message Last commit…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Jensen Huang’s five-layer cake explains how intelligence is manufactured. The missing layer explains how quickly - and by whom - it can scale.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：GLM 5.3 Flash | Model APIs | RunInfra RunInfraby RightNow © 2026 RunInfra. All rights reserved. Join the communitySystem status Backed by Combinator AICPA Type II SOC 2 Ask AI about RunInfra Part of RightNow RunInfraby…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：China & AI | WireScreen Briefings CareersProduct WWIRESCREEN · SPECIAL REPORT · CHINA AND ARTIFICIAL INTELLIGENCE WS-2026-034 · AUGUST 2026 Built and Owned The people, the money and the ownership behind China’s leading…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：AI factories must support increasingly large models and more complex reasoning workloads. To keep up with the insatiable compute demands of AI workloads, hyperscalers and AI-native companies are developing custom AI acc…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Google Research and UNSW Sydney released GlucoFM, a self-supervised foundation model that splits a CGM trace into a slow physiological stream and a transient event stream instead of encoding it as one sequence. At 0.72M parameters it reached 58.8 task-averaged PR-AUC across 14 cohort–task evaluations, beating a 135M GluFormer and a 385M MOMENT. It remains a research prototype with no regulatory clearance. The post Google Research Introduces GlucoFM: A 0.72M-Parameter Dual-Stream Foundation Model for Continuous Glucose Monitoring appeared first on MarkTechPost.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.25192v1 Announce Type: new Abstract: We introduce CRESSim-Neo, a batched GPU simulation engine for surgical robotics and robot learning. CRESSim-Neo combines position-based simulation of rigid bodies, deformable tissues, fluids, and strands with batched rendering, surgery-specific sensing, and a GPU-resident data pipeline. The engine supports applications including tissue manipulation, fluid suction, suturing, cable-driven robots, and ultrasound image synthesis. Direct access to physics and rendering buffers enables GPU-resident robot learning and zero-copy PyTorch integration using DLPack. We demonstrate CRESSim-Neo across rigid-body, deformable-body, and fluid simulation tasks, including vision-based and surgical robot-learning scenarios. On an NVIDIA RTX 4090, the engine achieves up to 2.03 million environment steps per second for 8192 parallel CartPole environments, and scales to batched surgical scenarios involving tissue deformation, fluid interaction, and ultrasound sensing. Overall, CRESSim-Neo provides a unified and scalable platform for surgical simulation, synthetic data generation, and surgical robot learning.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.24935v1 Announce Type: new Abstract: Accurate identification of early-stage apple fruitlet anatomical structures, including the calyx, fruitlet body, and peduncle, is essential for robotic thinning, crop-load management, and other precision orchard operations. This study presents a lightweight multimodal vision-language framework that adapts TinyCLIP for fine-grained fruitlet anatomy classification in complex orchard environments. A dataset of 600 high-resolution RGB images collected from Scilate and Scifresh apple orchards was converted into 224 x 224 image patches and annotated for three anatomical classes. Domain-specific language prompts, such as ``a photo of a class,'' were used to guide multimodal alignment between orchard imagery and horticultural structures. A sliding-window inference strategy with a stride of 112 pixels aggregates patch-level predictions into spatial heatmaps, enabling interpretable whole-image localization of fruitlet components relevant to robotic thinning. Patch-level evaluation on an NVIDIA T4 GPU achieved F1-scores of 0.95 for calyx, 0.98 for fruitlet, and 0.85 for peduncle, with a macro-F1 score of 0.93. Deployment-oriented optimization using ONNX and TensorRT enabled efficient inference on NVIDIA Jetson hardware, preserved accuracy under INT8 quantization, and supported model sizes of approximately 127-137 MB with millisecond-level patch inference. These results demonstrate that lightweight vision-language models can provide interpretable and edge-deployable perception for automated fruitlet analysis and future robotic thinning systems. The source code and implementation details are publicly available at https://github.com/WilliamBu1/A-Lightweight-Vision-Language-Model-for-Early-Stage-Fruitlet-Classification-in-Apple-Orchards.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.25061v1 Announce Type: new Abstract: GPUs increasingly accelerate database systems, but query-specific peak performance still often relies on hand-written kernels. Existing LLM kernel benchmarks focus on machine learning operators, leaving irregular, heterogeneous, data-movement-heavy database-style operators untested. We introduce DataKernelBench, which translates SQL into validated PyTorch TorchPlan programs and evaluates LLMs that optimize either the core tensor-bounded snippet or the full query in CUDA or Triton through execution-guided repair. Across ten proprietary and open-weight models on TPC-H SF10 with an H100 GPU, the strongest full-query CUDA configuration achieves $2.11\times$ speedup over torch.compile at full pass rate. We find that higher-performing implementations commonly use kernel fusion and execution-strategy changes, stronger models benefit most from full-query specialization, and workload context matters more than hardware context. To handle data larger than GPU memory, we extend TorchPlan with Dask-cuDF for on-demand partition loading on TPC-H SF100 with four H100 GPUs, achieving $2.54\times$ speedup

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.24946v1 Announce Type: new Abstract: Macros constitute a large part of the core area in modern very large-scale integration (VLSI) designs. Moreover, macro positions have a significant impact on the final quality of result (QoR), and macro legalization is typically the final step in determining the macro positions. However, existing approaches related to macro legalization either lack robustness or incur substantial computational costs or neglect the regularity between macros. To address these limitations, we introduce MacroAgent. The novel framework is a four-stage approach: clustering, contour generation, template matching, and inter-cluster refinement. We propose leveraging Large Language Models (LLMs) to discover multiple, effective heuristic regularity-aware contour algorithms. This framework successfully generates robust and effective algorithmic solutions for macro legalization. Compared with state-of-the-art macro legalization works, experimental results on TILOS and Chipyard benchmarks demonstrate a 2 to 8 fold improvement in layout regularity, a 3% to 5% reduction in routed wirelength with comparable congestion after global routing, and significantly better robustness with an acceptable runtime. Furthermore, end-to-end evaluation through Cadence Innovus place-and-route confirms that the regularity improvements translate into tangible PPA gains, including 2.9% lower routed wirelength and 68.3% TNS improvement over the DREAMPlace macro legalization baseline; it also achieves 1.8% lower routed wirelength when integrated into the Innovus macro placement flow.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.24945v1 Announce Type: new Abstract: Recent years have witnessed remarkable achievements of Large Language Models (LLMs) in multiple domains, while the excessive resource requirements of LLMs hinder the deployment on resource-constrained devices. Although model quantization stands out as an effective approach, conventional quantization approaches typically incur severe performance degradation due to uniform bit-width or simple heuristic sensitivity evaluation. In this paper, we propose a novel Fisher information-based Adaptive Mixed Precision Weight Quantization approach, i.e., FAMPWQ, which performs layer-adaptive weight quantization for effective LLM inference on commodity GPUs. First, we propose a system model with a novel Fisher information metric to measure the layer-wise sensitivity to quantization. Second, we propose a reinforcement learning-based bit-width allocator in FAMPWQ, which generates an adaptive bit-width allocation strategy based on the Fisher information sensitivity metric. Extensive experiments on 7 models and 5 benchmarks demonstrate that FAMPWQ significantly outperforms 7 baseline approaches in terms of PPL (up to 3.39 smaller), accuracy (up to 6.87% higher), and LLM-as-a-judge comparison (up to 76% win rate).

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.24938v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models scale capacity for strong quality while keeping per-token compute bounded through sparse expert activation. Yet low-latency MoE serving is increasingly challenging, because it spans two inference phases with fundamentally different bottlenecks: prefill is dominated by token-wise expert computation, whereas decode is constrained by memory traffic from the batch-wise activated expert set. However, existing training-free acceleration methods optimize only a single resource proxy, either the experts each token executes or the experts a batch activates, and either discard the excluded experts' contribution or leave it only implicitly approximated. In this paper, we propose ExFold, a unified training-free expert-folding framework for jointly accelerating MoE prefill and decode. ExFold casts both prefill and decode as one budgeted output-approximation problem: execute only a phase-specific constrained expert set while projecting the contribution of budget-excluded experts onto retained experts using calibrated scalar projectors. Motivated by the observation that many expert outputs are directionally aligned but differ in magnitude, ExFold calibrates a pairwise scalar-projector matrix on unlabeled data and uses it at inference time to fold excluded expert contributions into retained experts. Under this view, prefill acceleration becomes token-level Top-K folding, and decode acceleration becomes batch-level expert-pool folding. The two phases differ only in how retained experts are selected, while excluded contributions are recovered by one shared folding mechanism. We implement ExFold as a plug-and-play plugin in vLLM, with a lightweight expert-folding CUDA kernel, delivering up to 1.41x TTFT and 2.45x TPOT speedups while retaining about 99% of the original average quality.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Open Source wins!

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The conference with hot chips and even hotter companies

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：0:00 / 0:00 Introducing S1: In-Context Learning for Robotics Unseen tasks10-minute horizonsOne video promptNo post-training 13-minute read Introduction The evolution of language modeling provides a blueprint for turning…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：<p><strong><a href="https://qwen.ai/blog?id=qwen3.8-flash-next">Qwen3.8-Flash-Next</a></strong></p> Another open weights model from Qwen. This one is "a multimodal MoE model that also serves as an early preview of the architecture used in Qwen4".</p> <p>It's pretty big: 125B tokens, but only 6B active which means it gets a pretty big performance boost.</p> <p>I've been trying it out on a DGX Spark using <a href="https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF">these Unsloth quantized models</a>. I'm still exploring the model - so far I've tried the 72.5GB UD-IQ1_S one (producing <a href="https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fgist.github.com%2Fsimonw%2Ff9c69ebdab90d8a45b8de4742cc7b840">these pelicans</a>) and the 78.9GB UD-Q2_K_XL (producing <a href="https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fgist.github.com%2Fsimonw%2F6ba7cbfc1a9336986703b41f7fccd73a">these</a>).</p> <p>My favorite so far was this xhigh reasoning effort one from UD-Q2_K_XL:</p> <p><img alt="Flat vector illustration: a white pelican with an orange beak and orange legs rides a red bicycle along a sandy path, a wicker basket on the handlebars holding a blue fish, with green rolling hills, a small tree and bushes, white clouds and a bright yellow sun in a blue sky behind it" src="https://static.simonwillison.net/static/2026-08-27/IMG_7667.png" /> <p><small></small>Via <a href="https://news.ycombinator.com/item?id=49448210">Hacker News</a></small></p> <p>Tags: <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a>, <a href="https://simonwillison.net/tags/qwen">qwen</a>, <a href="https://simonwillison.net/tags/pelican-riding-a-bicycle">pelican-riding-a-bicycle</a>, <a href="https://simonwillison.net/tags/ai-in-china">ai-in-china</a>, <a href="https://simonwillison.net/tags/nvidia-spark">nvidia-spark</a></p>

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：AI Assistant Instinct Hits $2.5 Billion Valuation In Weeks Amid VC Feeding Frenzy Editors' Pick VCs Are So Obsessed With This AI Assistant That Its Valuation Jumped Fivefold In Weeks A hot new AI agent called Instinct t…

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Artificial intelligence startup Deep Cogito Inc. today announced that it has raised $43 million in funding. TQ Ventures led the Series A round. It was joined by Benchmark, Nexus Venture Partners, Atreides Management, South Park Commons and Zscaler Inc., a publicly traded cybersecurity provider. The deal brings Deep Cogito’s total outside funding to more than […] The post Deep Cogito raises $43M to develop self-improving AI models appeared first on SiliconANGLE.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Nvidia's predicting it will pull in $108 billion in revenue within just a few months. It wouldn't be the first company to rake in over $100 billion in quarterly revenue - Amazon, Apple, and Alphabet have repeatedly reached the milestone. Nvidia said in its latest earnings report that it brought in a record $96.2 billion in overall revenue in the past quarter, a jump of over $10 billion from the previous quarter. Its data center revenue alone more than doubled year-over-year to a record $89 billion, and the company's profits more than doubled to $59.7 billion. Nvidia's "edge computing" category, which includes its consumer gam … Read the full story at The Verge.