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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.
. 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 retainin
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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.
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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.
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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.
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
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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.
ud 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 componen
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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.
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, wh
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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.
e 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.
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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.
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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.
s, 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.
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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.
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Anthropic Publicly Submits S-1 Registration Statement for Proposed IPO on Nasdaq
On October 26, 2026, Anthropic publicly submitted a Form S-1 registration statement, planning to list on Nasdaq under the ticker ANT. The prospectus filing disclosed that the company's revenue in the first half of 2026 was $16.3 billion, with operating profit of $974 million in the second quarter, while also listing an NVIDIA compute commitment of up to $120.5 billion. Subsequently, in its October 28 IPO filing, Anthropic acknowledged that its life sciences business lacks physical laboratory and drug development experience; Goldman Sachs serves as the lead underwriter for the prospectus; TechCrunch reported that after the IPO, public shareholders will have almost no say over board candidates. On October 29, the prospectus disclosed four previously undisclosed Agent safety incidents. On October 30, Reuters reported that its IPO fundraising cap may reach $100 billion; Steve Eisman accused the prospectus of deliberately not disclosing third-quarter data. On November 2, Anthropic submitted a revised S-1, disclosing approximately $895.4 billion in compute procurement and leasing commitments, and reporting third-quarter revenue of $16.956 billion, a year-over-year increase of 1,127%, with an operating loss of $567 million for the quarter; on the same day, another report said it plans to raise up to $3.5 billion from Japanese investors in the IPO.
spectus filing disclosed that the company's revenue in the first half of 2026 was $16.3 billion, with operating profit of $974 million in the second quarter, while also listing an NVIDIA compute commitment of up to $120.5 billion. Subsequently, in its October 28 IPO filing, Anthropic acknowledged that its life sciences business lacks physical laboratory and drug development experience; Goldman Sachs serves as the lead underwriter for the prospectus; TechCrunch reported that after the IPO, public
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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.
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 implementa
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