Search “NVIDIA”

NVIDIA launches DSX POD modular AI factory reference design

On October 21, 2026, NVIDIA unveiled the DSX POD modular AI factory reference design at the GTC conference in Berlin, targeting Vera Rubin AI factories. The design is integrated and validated by partners inside modular containers before delivery, moving installation work such as compute, networking, cabling, and liquid cooling forward into the manufacturing stage. NVIDIA's press release states that the solution can shorten deployment time at a ready site from six months to as little as 14 days; a same-day report by Zhidongxi mentioned that Jensen Huang said adopting the solution can compress the time from compute arriving on site to going into production from 4 to 5 months to about 1 month. Partners include Dell, HPE, Lenovo, Foxconn (Ingrasys), QCT, and others, with global deliveries planned for early 2027. There are currently no subsequent developments.

NVIDIA launches DSX POD modular AI factory reference design

On October 21, 2026, NVIDIA unveiled the DSX POD modular AI factory reference design at the GTC conference in Berlin, targeting Vera Rubin AI factories. The design is integrated and validated by partners inside modular containers before delivery, moving installation work such as compute, networking, cabling, and liquid cooling forward into the manufacturing stage. NVIDIA's press release states that the solution can shorten deployment time at a ready site from six months to as little as 14 days; a same-day report by Zhidongxi mentioned that Jensen Huang said adopting the solution can compress the time from compute arriving on site to going into production from 4 to 5 months to about 1 month. Partners include Dell, HPE, Lenovo, Foxconn (Ingrasys), QCT, and others, with global deliveries planned for early 2027. There are currently no subsequent developments.

View story:NVIDIA launches DSX POD modular AI factory reference design

Evan Conrad says NVIDIA cannot build its own compute trading market

On October 21, 2026, SF Compute CEO Evan Conrad argued on the Latent Space podcast that NVIDIA cannot build its own compute trading market. His reasoning is that companies with large hardware or inference businesses find it difficult to operate a neutral exchange. In the same appearance, Conrad also said that the compute market must first achieve physical delivery—that is, actually delivering usable GPU compute—before discussing financialization; otherwise, market manipulation and basis risk are likely to arise. He explained that SF Compute is expanding from subleasing GPU clusters to building its own cloud operations capability in order to achieve physical delivery of GPU contracts, so that buyers receive usable compute rather than cash settled against an index; the company does not hold hardware in order to remain neutral, and it already supports customers in partitioning InfiniBand fabric online themselves. He also said that SF Compute has placed 5% of its capacity on the order book, that customers who spend $100 to buy capacity can on average recover $25 by reselling it, and that hundreds of millions of dollars in volume has already been traded. There are currently no subsequent developments.

View story:Evan Conrad says NVIDIA cannot build its own compute trading market

Evan Conrad says NVIDIA cannot build its own compute trading market

On October 21, 2026, SF Compute CEO Evan Conrad argued on the Latent Space podcast that NVIDIA cannot build its own compute trading market. His reasoning is that companies with large hardware or inference businesses find it difficult to operate a neutral exchange. In the same appearance, Conrad also said that the compute market must first achieve physical delivery—that is, actually delivering usable GPU compute—before discussing financialization; otherwise, market manipulation and basis risk are likely to arise. He explained that SF Compute is expanding from subleasing GPU clusters to building its own cloud operations capability in order to achieve physical delivery of GPU contracts, so that buyers receive usable compute rather than cash settled against an index; the company does not hold hardware in order to remain neutral, and it already supports customers in partitioning InfiniBand fabric online themselves. He also said that SF Compute has placed 5% of its capacity on the order book, that customers who spend $100 to buy capacity can on average recover $25 by reselling it, and that hundreds of millions of dollars in volume has already been traded. There are currently no subsequent developments.

The German federal government plans to cooperate with Nvidia

On October 16, 2026, the German federal government plans to cooperate with Nvidia on industrial AI applications and is prepared to invest a sum in the triple-digit millions of euros. The project focuses on what Nvidia calls "physical AI," that is, AI such as robots and automated industrial equipment that can interact with the real world, while also developing high-performance AI models for the factory of the future; German companies such as Siemens also plan to participate. On October 20, the German federal government and NVIDIA signed a joint declaration of intent to cooperate on frontier models for physical AI and drive the scaled deployment of industrial AI in Germany and Europe; NVIDIA also announced plans to invest in four German startups—Agile Robots, Avelios Medical, Mirelo, and Reflex Aerospace—as part of its nearly $10 billion European investment. On October 21, at GTC Berlin, NVIDIA announced that European ecosystem partners are adopting the NVIDIA DSX AI factory platform.

SGLang natively supports and powers the Beam production API

On October 16, 2026, LMSYS announced that SGLang now natively supports Reflection's Beam model and is being used to power its production API. Beam is a 501B-parameter MoE model with 23B active parameters per token and a native 1M-token context window, planned for release under Apache 2.0; SGLang provides BF16, NVFP4, and dynamic FP8 support for it, along with DFlash2 speculative decoding and hybrid attention prefix caching. Running OpenHands multi-turn agentic coding replay with NVFP4 on 4 NVIDIA GB300 GPUs, it reached 127.6K tokens/sec per GPU and 185.87 tokens/sec per user (p50) at a concurrency of 32. On the same day, Reflection released the Beam model weights, with Ollama offering them free for a limited time as a day-zero partner with zero data retention; Beam has approximately 501 billion total parameters, about 23 billion active parameters per token, a 1 million-token context window, and is released under the Apache 2.0 license. Subsequent information from AWS states that the Beam weights have been released on Hugging Face, offering three checkpoints—BF16, FP8, and NVFP4—and are now available on Amazon SageMaker JumpStart, and can also be accessed via SageMa

Nvidia joins forces with six financial institutions to establish an AI infrastructure financing platform

On October 16, 2026, the German federal government plans to cooperate with Nvidia on industrial AI applications and is prepared to invest a sum in the triple-digit millions of euros. The project focuses on what Nvidia calls "physical AI," that is, AI such as robots and automated industrial equipment that can interact with the real world, while also developing high-performance AI models for the factory of the future; German companies such as Siemens also plan to participate. On October 20, the German federal government and NVIDIA signed a joint declaration of intent to cooperate on frontier models for physical AI and drive the scaled deployment of industrial AI in Germany and Europe; NVIDIA also announced plans to invest in four German startups—Agile Robots, Avelios Medical, Mirelo, and Reflex Aerospace—as part of its nearly $10 billion European investment. On October 21, at GTC Berlin, NVIDIA announced that European ecosystem partners are adopting the NVIDIA DSX AI factory platform.

View story:The German federal government plans to cooperate with Nvidia

English translation is being prepared.

English translation is being prepared.

View story:English translation is being prepared.

Anthropic Publicly Submits S-1 Registration Statement for Proposed IPO on Nasdaq

On October 26, 2026, Anthropic publicly submitted its Form S-1 registration statement, planning to list on Nasdaq under the ticker ANT. The prospectus discloses that its revenue grew from $41 million in 2023 to $16.3 billion in the first half of 2026, with $11.6 billion in the second quarter alone and operating profit of $974 million; about 48% of first-half revenue flowed through Bedrock, Vertex, and Azure Marketplace, and it lists NVIDIA compute commitments of up to $120.5 billion. Subsequently, on October 27, World Trade Securities estimated that its cash reserves could approach $400 billion by the end of 2027; in the cover letter of its IPO filing, Anthropic reiterated that Claude will not display ads, and acknowledged in the filing that its life sciences business lacks physical laboratory experience. On October 28, Goldman Sachs secured the lead-left seat on the prospectus, and a CITIC Securities research report described the listing as providing a key anchor for the AI industry. On October 29, the prospectus disclosed four previously unreported Agent safety incidents. On October 30, Reuters reported that its IPO fundraising cap could reach $100 billion; Steve Eisman accused the prospectus of deliberately omitting third-quarter data, and Anthropic has not yet responded.

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.

DeepSeek open-sources inference framework DeepInfer

On October 20, 2026, DeepSeek announced the open-sourcing of its internal production-grade inference framework, DeepInfer. According to Latent Space, the framework powers DeepSeek-V4.1-Flash, is customized based on vLLM, supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, and targets the NVIDIA SM100 and Huawei Ascend 950 platforms; the published single-cluster production figures are 155 trillion tokens processed per day, a 96% input cache hit rate, 400 ms first-token latency, and a generation speed of 230 tokens/s. The report also noted that components such as KV cache management and load balancing have not yet been fully open-sourced. The next day, DeepSeek published the DeepInfer technical report on Zhihu, disclosing internal details including the hardware selection for Prefill and Decode and the KVCache targets, and providing data showing a single cluster serving 155 trillion Tokens/day at $38 per card per hour at off-peak prices. The framework is currently open-sourced, but the KV cache system, Prefill/Decode disaggregation, and load balancing required for a complete service are not fully included in this open-source project.

English translation is being prepared.

View story:English translation is being prepared.