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Inception releases flagship diffusion language model Mercury 3
Inception AI Inc. releases its flagship diffusion language model Mercury 3. Information published by OpenRouter on October 20 shows that Mercury 3 is now available on the platform, with a generation speed exceeding 1000 tok/s, a context window of 1M, and a speed 4 times that of Mercury 2.5; Inception claims that it surpasses GPT-6 Luna and Haiku 5.5 on IFBench, GPQA Diamond, and SWE-Bench Atlas, and during its launch period the pricing is $0.10/M tokens for input and $0.35/M tokens for output, equivalent to a 50% discount. SiliconANGLE reported on October 21 that Inception outperforms GPT-6 Luna and Claude Haiku 5.5 on 8 of its own 12 benchmarks, with speeds 4 to 8 times faster; the company says it reaches 1068 tokens per second on NVIDIA GPUs, compared with about 240 for Haiku 5.5 and about 130 for GPT-6 Luna; the context window has been increased to 1 million tokens, the parameter count was not disclosed, with only a claim that it exceeds Mercury 1 by more than 40 times, and pricing is 2 per million input tokens
SiliconANGLE reported on October 21 that Inception outperforms GPT-6 Luna and Claude Haiku 5.5 on 8 of its own 12 benchmarks, with speeds 4 to 8 times faster; the company says it reaches 1068 tokens per second on NVIDIA GPUs, compared with about 240 for Haiku 5.5 and about 130 for GPT-6 Luna; the context window has been increased to 1 million tokens, the parameter count was not disclosed, with only a claim that it exceeds Mercury 1 by more than 40 times, and pricing is 2 per million input tokens
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Matt Turck announced that he is leading Daytona's Series B funding round
Matt Turck announced that he is leading Daytona's Series B funding round. According to Fortune Tech, Daytona, which provides cloud sandboxes for AI agents, has completed a $60 million Series B funding round at a valuation of $1 billion, led by FirstMark, bringing its total funding to approximately $91 million. Matt Turck said that FirstMark led the Series A round earlier this year and is now leading the Series B round as well. The report also said that Daytona's customers include Anthropic, OpenAI, Nvidia, Cursor, Cognition, Meta, and Cohere; CEO Burazin said the company's revenue run rate grew 25-fold between January and September, and said it can provision a new sandbox for an agent within 60 milliseconds.
tion of $1 billion, led by FirstMark, bringing its total funding to approximately $91 million. Matt Turck said that FirstMark led the Series A round earlier this year and is now leading the Series B round as well. The report also said that Daytona's customers include Anthropic, OpenAI, Nvidia, Cursor, Cognition, Meta, and Cohere; CEO Burazin said the company's revenue run rate grew 25-fold between January and September, and said it can provision a new sandbox for an agent within 60 milliseconds.
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AMD executive argues physical AI evaluation should prioritize real-time control and reliability
AMD executive Salil Raje proposed that the performance evaluation of physical AI should shift from raw compute power to real-time control and system-level reliability. The claim was made around October 19, 2026, and related reports were published on October 20. According to the reports, AMD builds heterogeneous architectures with Kria AI SOM, Versal, Zynq, and Spartan FPGAs; in third-party tests, the Kria AI SOM achieved inference times under 0.1 seconds and supported 8,000 control decisions per second; in open navigation benchmarks, the Ryzen AI Embedded configuration achieved control timing reliability up to 3.4 times that of Nvidia Jetson T5000, with 1.6 times more available compute headroom. There have been no subsequent developments so far.
er 20. According to the reports, AMD builds heterogeneous architectures with Kria AI SOM, Versal, Zynq, and Spartan FPGAs; in third-party tests, the Kria AI SOM achieved inference times under 0.1 seconds and supported 8,000 control decisions per second; in open navigation benchmarks, the Ryzen AI Embedded configuration achieved control timing reliability up to 3.4 times that of Nvidia Jetson T5000, with 1.6 times more available compute headroom. There have been no subsequent developments so far.
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Nebius invests $10 million to support European AI developers
Nebius announces $10 million to support European AI developers. According to a report dated October 19, 2026, Nebius's AI Builder Program will expand to Europe, providing a total of $10 million in support, covering compute grants, capital, and community programs. The compute grants target areas such as open-source models, coding agents, post-training, and evaluation; in addition, it offers connections with European venture capital firms such as Accel, as well as hackathons and demo days co-hosted with communities such as NVIDIA and AI Tinkerers. There are currently no subsequent developments.
19, 2026, Nebius's AI Builder Program will expand to Europe, providing a total of $10 million in support, covering compute grants, capital, and community programs. The compute grants target areas such as open-source models, coding agents, post-training, and evaluation; in addition, it offers connections with European venture capital firms such as Accel, as well as hackathons and demo days co-hosted with communities such as NVIDIA and AI Tinkerers. There are currently no subsequent developments.
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Helion supports joint search of compiler controls and kernel configurations
Helion now supports incorporating compiler control parameters and kernel configurations into a single search. According to a PyTorch Blog article dated October 19, 2026, PyTorch Helion searches ptxas compiler control parameters through NVIDIA CompileIQ, jointly with the original kernel configuration search, further tapping performance headroom on about 27 kernels that have already been pre-tuned. CompileIQ packages tuning results as a portable Advanced Control File (ACF); Helion supports both specifying a fixed ACF and searching multiple ACFs together as one dimension of autotuning. These compiler controls have been exposed by ptxas since CUDA 13.3. There are currently no subsequent developments.
eters and kernel configurations into a single search. According to a PyTorch Blog article dated October 19, 2026, PyTorch Helion searches ptxas compiler control parameters through NVIDIA CompileIQ, jointly with the original kernel configuration search, further tapping performance headroom on about 27 kernels that have already been pre-tuned. CompileIQ packages tuning results as a portable Advanced Control File (ACF); Helion supports both specifying a fixed ACF and searching multiple ACFs togethe
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SageMaker JumpStart offers a curated catalog of AI models
Amazon SageMaker JumpStart provides a curated catalog of AI models, solution templates, and example notebooks. According to a retrospective published by AWS Machine Learning on October 20, 2026, from January to September 2026, the catalog added more than 100 models from 30 providers, of which Alibaba (Qwen) contributed 29, NVIDIA 17, Mistral 7, Google 6 Gemma 4 variants, IBM 4, and Cohere 4. The newly added models cover multimodal, speech, image, and video generation, and introduce quantized and compact models such as GLM-5.2 FP8 and JetBrains Mellum2-12B to reduce inference costs. The catalog currently retains the above additions.
published by AWS Machine Learning on October 20, 2026, from January to September 2026, the catalog added more than 100 models from 30 providers, of which Alibaba (Qwen) contributed 29, NVIDIA 17, Mistral 7, Google 6 Gemma 4 variants, IBM 4, and Cohere 4. The newly added models cover multimodal, speech, image, and video generation, and introduce quantized and compact models such as GLM-5.2 FP8 and JetBrains Mellum2-12B to reduce inference costs. The catalog currently retains the above additions.
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n8n blog introduces running AI workflows locally on DGX Spark
On October 19, 2026, the n8n blog published a tutorial on how to deploy a complete model stack locally on a single NVIDIA DGX Spark, enabling n8n workflows to handle transcription, OCR, document Q&A, and image generation without calling cloud APIs. The tutorial states that the entire stack exposes an OpenAI-compatible endpoint through LiteLLM, with the main model being the NVFP4 version of Qwen3.6-35B-A3B, achieving a generation speed of 120 tokens/s for a single user and 359 tokens/s combined for eight users. The author's hands-on testing found that image generation reduces conversation speed to about one-third, and recommends that small teams queue image tasks or schedule them outside working hours. Currently, this content has only the n8n blog as its source.
On October 19, 2026, the n8n blog published a tutorial on how to deploy a complete model stack locally on a single NVIDIA DGX Spark, enabling n8n workflows to handle transcription, OCR, document Q&A, and image generation without calling cloud APIs. The tutorial states that the entire stack exposes an OpenAI-compatible endpoint through LiteLLM, with the main model being the NVFP4 version of Qwen3.6-35B-A3B, achieving a generation speed of 120 tokens/s for a single user and 359 tokens/s combined f
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Meta releases technical details of the Prometheus supercluster
Meta's engineering team has published technical details of the Prometheus supercluster. Located in New Albany, Ohio, the cluster currently consists of hundreds of thousands of GPUs and operates as a single machine, with plans to scale to approximately 1 million GPUs. Meta confirmed that the first phase has already used the cluster to train the Muse family of foundation models (including Muse Wonder), with Muse Wonder achieving pretraining goodput of over 85% and reinforcement learning goodput of over 90%. Meta also disclosed two platforms: Catalina, based on NVIDIA GB200 NVL72, and Clemente, based on GB300 NVL72, as well as the upcoming Vera Rubin NVL72 platform.
s of GPUs and operates as a single machine, with plans to scale to approximately 1 million GPUs. Meta confirmed that the first phase has already used the cluster to train the Muse family of foundation models (including Muse Wonder), with Muse Wonder achieving pretraining goodput of over 85% and reinforcement learning goodput of over 90%. Meta also disclosed two platforms: Catalina, based on NVIDIA GB200 NVL72, and Clemente, based on GB300 NVL72, as well as the upcoming Vera Rubin NVL72 platform.
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Nebius Expands AI Builder Program to Europe
Nebius expands its AI Builder Program to Europe. According to a Testing Catalog report on October 19, 2026, the program partners with NVIDIA, Accel and other European venture capital firms to provide a combined $10 million in compute grants, funding and community program support. The program is free to join; developers can receive credits for running open-source models on Nebius, and selected projects can also receive compute grants at Token Factory. The European edition additionally supports larger open-source projects that need more compute or funding, and hosts hackathons and demo days with communities such as AI Tinkerers, {Tech: Europe} and ESRA. There are currently no subsequent developments.
Nebius expands its AI Builder Program to Europe. According to a Testing Catalog report on October 19, 2026, the program partners with NVIDIA, Accel and other European venture capital firms to provide a combined $10 million in compute grants, funding and community program support. The program is free to join; developers can receive credits for running open-source models on Nebius, and selected projects can also receive compute grants at Token Factory. The European edition additionally supports la
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Meta says it is training the Muse foundation model on the Prometheus supercomputing cluster
Meta announced that the first phase of its gigawatt-scale supercomputing cluster Prometheus has been completed in New Albany, Ohio, and is being used to train the Muse series of foundation models, including Muse Wonder. The cluster currently consists of hundreds of thousands of GPUs, with a full-scale target of about 1 million, and has deployed two generations of platforms, NVIDIA GB200 NVL72 (Catalina) and GB300 NVL72 (Clemente), with Vera Rubin NVL72 to be introduced later. Meta says Muse Wonder's pre-training goodput exceeds 85%, reinforcement learning goodput exceeds 90%, and the interruption rate has been reduced by 99% from its mid-2025 peak. Subsequent reports added that during Muse Wonder training, an interruption occurred on average only about every 47K GPU hours; Meta said that in a cluster of this scale, even a single host failing to meet performance standards would slow down an entire training round, so Prometheus was designed for large-scale fault tolerance.
including Muse Wonder. The cluster currently consists of hundreds of thousands of GPUs, with a full-scale target of about 1 million, and has deployed two generations of platforms, NVIDIA GB200 NVL72 (Catalina) and GB300 NVL72 (Clemente), with Vera Rubin NVL72 to be introduced later. Meta says Muse Wonder's pre-training goodput exceeds 85%, reinforcement learning goodput exceeds 90%, and the interruption rate has been reduced by 99% from its mid-2025 peak. Subsequent reports added that during Mus
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n8n blog introduces running AI workflows locally on DGX Spark
On October 19, 2026, the n8n blog published a tutorial on how to deploy a complete model stack locally on a single NVIDIA DGX Spark, enabling n8n workflows to handle transcription, OCR, document Q&A, and image generation without calling cloud APIs. The tutorial states that the entire stack exposes an OpenAI-compatible endpoint through LiteLLM, with the main model being the NVFP4 version of Qwen3.6-35B-A3B, achieving a generation speed of 120 tokens/s for a single user and 359 tokens/s combined for eight users. The author's hands-on testing found that image generation reduces conversation speed to about one-third, and recommends that small teams queue image tasks or schedule them outside working hours. Currently, this content has only the n8n blog as its source.
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