Search “NVIDIA”

Cohere announces an additional C$4 billion investment and 1,500+ jobs in Canada

On October 30, 2026, Cohere announced progress on its Canadian investment plan, expecting to invest more than C$4 billion in R&D and AI infrastructure by 2031 and create more than 1,500 jobs, of which about 1,000 will be technical roles in AI research, engineering, and product development. The plan began in 2024 and will also provide more than 250 paid student positions by 2029, along with research partnerships with the University of Toronto, the University of Waterloo, and others. Cohere has entered into a C$309 million GPU cloud partnership with Bell AI Fabric to run its models on Canadian sovereign infrastructure. On the same day, Unite.AI reported that the Canadian government had announced the previous day an equity investment of up to C$1.6 billion, joined by private investors including NVIDIA, Bell, and Schwarz Group, with the investment made in stages and regular reporting on employment and investment progress; under the agreement, Cohere must create at least 1,000 high-paying technical jobs by 2030 and invest at least an additional C$1.7 billion in computing infrastructure in Canada, while retaining its Canadian registration and headquarters.

X.PIN Announces Benchmark Test Results for Iluvatar CoreX Tiange 300

On October 30, 2026, X.PIN relayed and published benchmark test data for the Iluvatar CoreX Tiange 300. Its report claimed that in standard inference tests, the Tiange 300's throughput was 1.05 times that of the Nvidia H200; under MoE workloads, performance improved by 15% and first-token latency decreased by 30%; and when fine-tuning Qwen3-32B, its speed was 1.33 times that of the H100. There are currently no follow-up developments or official responses.

Rohan Paul says total AI chip compute capacity grows 3.4 times annually

Citing Epoch AI data, Rohan Paul pointed out that the total compute capacity of all AI chips in use grows 3.4 times annually. He also stated that if the compute capacity of newly purchased chips grows only 2.5 times annually, this total will eventually slow to about 2.5 times as well. The original Epoch AI post stated that since early 2024, the compute capacity of AI chips sold each quarter has grown 2.5 times annually, lower than the roughly 6 times driven by the Nvidia H100 in 2023.

Teradyne Robotics and Sereact Announce Partnership to Advance Physical AI

On October 28, 2026, Teradyne Robotics and Sereact announced a formal partnership at Nvidia GTC Berlin 2026 to run Sereact's physical AI platform Cortex on Universal Robots' collaborative robots for unstructured tasks such as returns processing and picking. According to Robotics & Automation News, the two parties have delivered dual-arm systems composed of hundreds of UR robots to European customers including Zalando, and are autonomously processing returned goods at Zalando fulfillment centers in Greven, Germany, and Świebodzin, Poland. Cortex is a VLA model trained on Nvidia Isaac Sim and Cosmos, and runs on Nvidia Jetson Thor at deployment. There are currently no subsequent developments.

Akamai Launches Beta Versions of Akamai Inference and Agent Sandbox

On October 29, Akamai released beta versions of two products, Akamai Inference and Akamai Agent Sandbox, allowing users to run AI models on Akamai Cloud and securely execute code generated by AI agents. According to The Elec, Akamai Inference supports 12 open-weight models, including OpenAI's gpt-oss-120b, Google's Gemma 4, and Nvidia's Nemotron 3 Ultra, and provides an OpenAI-compatible API; Agent Sandbox is based on WebAssembly isolation technology and restricts code access to computing resources, network connections, and system functions. Analytics India Magazine separately reported that Agent Sandbox is released as an open-source component of the Spin framework, and that Akamai is also expanding its NVIDIA Blackwell Ultra GPU cluster, with each eight-GPU node equipped with 2.3 TB of HBM3e memory. Both services are currently in the beta stage.

Eclypsium demonstrates that compromised hardware can tamper with AI models at runtime

Eclypsium demonstrated how compromised computing infrastructure can alter AI model behavior during inference. The experiment was completed in an NVIDIA Grace Hopper environment with NVIDIA's support; attackers used a compromised DPU and lax memory isolation to modify runtime model weights during inference, changing the model's answers while the model files on disk and hash verification still appeared normal. The experiment relied on conditions such as IOMMU passthrough and lax PCIe isolation, which the researchers explicitly noted are not Linux default settings. The report brings the DPU, GPU, management controller and their firmware into the AI security boundary, and notes that model signing, secure boot, runtime isolation and continuous monitoring each answer different questions. There are currently no further developments.

LG CNS Reports Third Quarter Revenue and Operating Profit Growth

LG CNS announced its third quarter consolidated results, with revenue of 1.6643 trillion won and operating profit of 138.2 billion won, up 9.3% and 15.0% year-on-year respectively, while the operating margin rose to 8.3%. According to The Elec, revenue from the financial IT business grew 21.3% to 408.2 billion won, and revenue from the AI and cloud business grew 9.3% to 961.5 billion won, accounting for 57.8% of total revenue. The same report also mentioned that in September the company decided to purchase equipment worth 381.4 billion won, including Nvidia's next-generation Vera Rubin GPUs, for building an AI factory. There are currently no subsequent developments in this matter.

New York Times investigation claims Nvidia CEO Jensen Huang may be a target of Chinese espionage

On October 30, 2026, Nvidia responded to an investigative report by The New York Times. The report said that when U.S. intelligence agencies were investigating the chip transshipment issue, they surveilled individuals close to Nvidia CEO Jensen Huang and identified Alice Huang, an executive at the Singapore cloud service provider Megaspeed, as a suspected Chinese government intelligence officer. An Nvidia spokesperson said that Megaspeed is merely a regional cloud customer of Nvidia, that it is normal for Jensen Huang to communicate with thousands of customers each year, and that allegations that Megaspeed evaded export controls were thoroughly investigated and clarified last year, and that customers cannot access Nvidia's trade secrets or related national security information. Nvidia also said that if the U.S. government believes anyone poses a threat to American industry, it will inform the company.

View story:Nvidia responds to report that U.S. intelligence agencies surveilled people close to Jensen Huang

PyTorch Foundation releases PyTorch 2.15

On October 29, 2026, the PyTorch Foundation released PyTorch 2.15. According to information published on the PyTorch Blog the same day, this version switches all process groups that use nccl to the rewritten NCCL backend by default, and includes a flight recorder that dumps on-site information when collective communication hangs, with fallback available via TORCH_DIST_USE_NCCL2=0 or nccl-legacy. This version also adds float8_e4m3fn and _scaled_mm support for Apple Silicon, and the new grouped_mm improves MoE decoding throughput by 1.32–1.34x on M3 Ultra. The default wheel is upgraded to CUDA 13.2, while a CUDA 13.4 wheel is also provided with experimental support for NVIDIA Rubin GPUs; a ROCm 10.0 wheel is newly added, and Python 3.10 and macOS 14 are no longer supported. There are currently no subsequent developments.

Block releases full-duplex speech model Dessa

Block's AI research lab has released the full-duplex speech model Dessa, opening a waitlist as a research preview. According to MarkTechPost, the model was trained only on human voice conversation audio, without using text or pretrained models, and can run locally on a MacBook Pro M4 Max or higher configuration, faster than real time; training used one NVIDIA GB200 NVL72 (72 GPUs). No benchmark scores, parameter count, weights, paper, or API have been released so far.

OpenBMB open-sources ForgeKernel, after previously saying it looks forward to more results from the Forge series

On October 28, 2026, ModelBest's OpenBMB reposted a message from Yuxuan Li about the ForgeKernel kernel optimization framework, saying it looks forward to the forthcoming paper and more results from the Forge series. The framework belongs to the Forge series and is said to rank first in tests on NVIDIA SOL-ExecBench (FlashInfer-Bench) and Huawei CANN-Bench. On October 30, ModelBest announced the open-sourcing of ForgeKernel; its agent completed 42 rounds of iteration in about 22 hours, producing 32 kernel versions, and achieved 1.098x performance on NVIDIA B200 for the DeepSeek V4.1 FlashMLA Prefill implementation, at 472 microseconds versus a baseline of 518 microseconds. The open-source release includes the optimized fused prefill kernel, the kernels covered in the paper spanning attention, Gated DeltaNet, Kimi Delta Attention, GEMM and convolution, as well as the ForgeKernel harness that can be used to optimize one's own kernels.

SoMark open-sources the 1.3B document parsing model OXR-1.0

SoMark has open-sourced OXR-1.0, a 1.3B-parameter document parsing model that can convert PDFs and page images into Markdown or JSON. The weights have been released on Hugging Face, and lightweight testing can run on a single 24GB GPU. According to Rohan Paul, the model ranks first on olmOCR-Bench, OmniDocBench, and DocParsingBench, achieving a decoding speed of 558 tokens/s on a single NVIDIA RTX PRO 6000 GPU and parsing 100 pages end-to-end in about 101 seconds; its post-training stage employs a recursive self-improvement (RSI) loop, in which a general-purpose VLM judges parsing results and generates newly annotated pages, eliminating the need for manual annotation. QbitAI reports that the OXR main model open-sourced by the SoMark team has only 1.3B parameters and topped all three benchmark tests, with full parsing speed exceeding that of PaddleOCR-VL-1.6 and MinerU2.5-Pro, which were also evaluated in the same comparison. It covers 13 categories of document elements, including text, tables, formulas, chemical structure formulas, and code blocks, and can parse reading order and output layout coordinates.

Survival LLM releases offline AI emergency assistant

On October 28, 2026, Survival LLM released a free, open-source AI assistant and emergency reference library that runs entirely locally, and can still answer questions about food storage, burn treatment, water purification, and more after going offline, listing the articles its answers are based on. When the app starts, it detects the computer's memory and downloads a matching model, ranging from IBM Granite 4.2 8B on devices with 16–31 GB of memory to NVIDIA Nemotron 3.5 Super on Macs with 128 GB or more; the reference library is about 9 GB and contains roughly 930,000 Wikipedia articles, FEMA and CDC guidelines, military medical manuals, and iFixit repair guides. According to the report, in a blind test of 22 power-outage and first-aid questions, Nemotron 3.5 Super and Qwen3.8 27B each answered 21 correctly, while Granite 4.2 8B answered 14 correctly. There are currently no further developments.

Meta plans to raise up to $95 billion through an equity offering

On October 26, 2026, Meta Platforms announced plans to raise up to $95 billion through an equity offering, to be used for AI infrastructure, computing power, and personal superintelligence. The first round of transactions totaled $35 billion, of which about two-thirds came from common stock, and $12.5 billion in Series A depositary shares were issued; subsequently, it plans to raise up to an additional $60 billion through an at-the-market offering. NVIDIA, AMD, and Broadcom expressed interest in subscribing to a combined total of up to $5 billion in the Class A offering, but this is non-binding. On October 27, AMD announced a deep strategic partnership with Meta and invested $1 billion in Meta's equity offering. On October 28, Meta's $25 billion common stock offering was about five times oversubscribed.

Meta Shares AI Networking Innovations at OCP Summit

At the 2026 OCP Global Summit, Meta unveiled networking innovations supporting its latest AI models and its gigawatt-scale Prometheus cluster. The first liquid-cooled network rack introduced by Meta, IcePack, debuted alongside the 102.4T switch IceCube (128×800G, available with either Broadcom Tomahawk 6C or NVIDIA Spectrum-6). For scale-out, the Flattened Planar Fabric connects each GPU to eight network planes, simplifying the Clos architecture from 3 tiers to 2; for scale-across, Meta introduced BAG, a petabit-class super backbone connecting buildings, campuses and regions. Meta also introduced Tahan, an MTIA 300 Ethernet scale-up switch based on Broadcom Tomahawk5, and open-sourced its MetaRoCE software implementation, with the first user-space version, libsoftmetaroce, now available on GitHub.

Block releases full-duplex speech model Dessa

Block's internal AI research lab released the full-duplex speech-to-speech model Dessa on October 23. According to Unite.AI, the model was trained entirely from scratch on human conversational speech, can listen and speak simultaneously, and natively perceives emotion, sarcasm, and laughter; its inference is faster than real time and it can run locally on a MacBook Pro M4 Max or higher configuration, with the team completing training on a single NVIDIA GB200 NVL72. Block has released demo recordings and a public waitlist, and has opened early access to a small number of AI safety researchers. Other reports note that the model does not rely on a text intermediary and supports full-duplex conversation with the ability to interrupt and interject at any time, and that training consumes hundreds of times fewer tokens than large language models, but the author points out that this is currently only a small-scale research preview from Block's internal lab, the demos were selected by the company, and questions remain about how pure audio models perform on complex multi-step logical reasoning.

DeepSeek open-sources inference framework DeepInfer

On October 20, 2026, DeepSeek open-sourced its internal inference framework DeepInfer. According to Yuchen Jin, citing data disclosed by DeepSeek, a single cluster processes 155 trillion tokens per day and generates $38 in revenue per GPU-hour (off-peak pricing); DeepInfer supports both NVIDIA Blackwell and Huawei Ascend. A subsequent 36Kr report stated that its accompanying open-source deepseek-recipe supports NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs. On October 21, swyx, citing Latent.Space data, said a single cluster processes 155T tokens per day with a 96% input cache hit rate, and argued that the lifetime value of each sold GPU has risen by about 30%. On October 24, Nathan Lambert said DeepSeek's single cluster processes 150T tokens per day, exceeding the combined open-source model inference volume of OpenRouter, Fireworks, Together, and BaseTen, and noted that the actual number of clusters is unknown; on the same day, LMSYS published a same-hardware benchmark comparison, stating that SGLang meets or exceeds DeepInfer on DeepInfer's own benchmark, with prefill throughput up to 1.04x and decode step length shortened by 1.1%–7.4% on 64x GB300.

Artificial Analysis publishes AA-AgentPerf results for Muse Spark Open

Artificial Analysis on October 24, 2026 published on AA-AgentPerf preliminary test results for Meta's newly open-weight model Muse Spark Open. The model is a 816B-parameter, 42B-active MoE, scoring 39 on the Artificial Analysis Intelligence Index. Tests show that NVIDIA B300 and GB300 each reach about 150M tokens/s per megawatt of power under throughput-first configurations; GB300 reaches about 108M and B300 about 92M at 100 tok/s output speed; AMD MI355X reaches 70M tokens/s per megawatt at 65 tok/s. Of the model's 62 layers, 46 use 2,048-token sliding window attention, reducing KV cache requirements at 1M context by about 75% versus full attention. Meta released an NVFP4 checkpoint, and AMD released an MXFP4 checkpoint. There are currently no subsequent development facts.

Schneider Electric launches prefabricated power modules for AI data centers

On October 23, 2026, Schneider Electric launched its next-generation prefabricated power modules and power skids for AI data centers. According to The Elec, the product is based on a reference design jointly developed with the Nvidia GB300 NVL72 AI server system, supports up to 142kW of power density per rack, and integrates power management and liquid cooling control. The product uses Galaxy VXL UPS technology and is offered in three capacities: 2MW, 2.25MW, and 2.5MW, with the 2.5MW version being its highest-capacity single-container prefabricated power solution to date. The product will be manufactured at a factory near Barcelona, Spain, launching first in Europe, entering North America later in 2026, and then expanding to Asia-Pacific. There are currently no subsequent developments.

ServeTheHome reviews the MSI XpertStation WS300

ServeTheHome reviewed the MSI XpertStation WS300. This machine is the second desktop workstation based on the NVIDIA DGX Station GB300, equipped with a Blackwell Ultra GPU with 252GB HBM3E (7.1TB/s), a 1600W power supply, and dual 400Gbps ConnectX-8 networking. The entire unit weighs nearly 30kg, uses a tower chassis with closed-loop liquid cooling, is priced above $100,000, and is positioned as server-grade hardware that can run local AI while sitting beside a desk. There are currently no subsequent developments.