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Thursday

  1. UCSD Hao AI Lab

    FastVideo FastH3 Omni Runs Locally on DGX Spark, RTX GPUs, and Apple MLX

    Hao AI Lab announced that FastVideo FastH3 Omni can run locally on NVIDIA DGX Spark, RTX GPUs, and Apple MLX. Generating a 5-second 480p video from a single photo and a prompt takes about 3 minutes, or about 2 minutes on two Sparks; FastH3 T2AV takes 52.6 seconds on a single Spark and 30.7 seconds on two Sparks. The clips in the demo video were all generated by DGX Spark using an 8-step NVFP4 checkpoint, and blog, Cookbook, and Hugging Face links are provided.

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  2. SiliconANGLE

    One-Year-Old AI Research Lab Flapping Airplanes Raises $1 Billion, Reportedly Valued at $6 Billion

    AI research lab Flapping Airplanes announced it has closed a new $1 billion funding round, with Bloomberg reporting a valuation of $6 billion; the company is about a year old and has no commercial product yet. In its first year the company focused mainly on developing new GPU primitives, saying its megakernel framework can complete full training, inference and game running within a single kernel, allowing memory-bound inference and compute-bound training to overlap on the same chip. The Series A round was co-led by Index Ventures and Lux Capital, with Sequoia Capital and GV, which participated in the $180 million seed round in January, continuing to invest, and Nvidia also among the institutional investors.

    first year the company focused mainly on developing new GPU primitives, saying its megakernel framework can complete full training, inference and game running within a single kernel, allowing memory-bound inference and compute-bound training to overlap on the same chip. The Series A round was co-led by Index Ventures and Lux Capital, with Sequoia Capital and GV, which participated in the $180 million seed round in January, continuing to invest, and Nvidia also among the institutional investors.

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  3. SiliconANGLE

    Era4 ties financial sovereignty to UK AI infrastructure

    Era4, through self-built energy, UK-based AI facilities and its own hardware, argues for giving the UK more control over the AI technology stack and keeping related revenue within the domestic economy. Its Chesterfield facility uses liquid-cooled Nvidia B300 systems supplied by HPE and runs HPE Private Cloud AI; the company also plans to build AI infrastructure on about 20% of a 230-acre former steelworks site in Newport, Wales.

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  4. Wired AI

    Recursive Releases Open-Source Coding Model Flint, Says Most of Development Process Was Completed by AI

    Recursive released Flint, its first open-weight coding model, with 40B parameters. CEO Richard Socher said that nearly every stage of the model pipeline was completed by its Eureka Machine system, including a new base training method, the model architecture, and code optimized for specific chips. A WIRED author tested it on a Mac with 48GB of memory, using it to build a command-line to-do app, a web research scraping program, and an RSI educational website, and found its speed and accuracy superior to models of the same size. Recursive has raised $650 million from Google Ventures, Greycroft, Nvidia, and AMD Ventures, at a valuation of $4.65 billion.

    completed by its Eureka Machine system, including a new base training method, the model architecture, and code optimized for specific chips. A WIRED author tested it on a Mac with 48GB of memory, using it to build a command-line to-do app, a web research scraping program, and an RSI educational website, and found its speed and accuracy superior to models of the same size. Recursive has raised $650 million from Google Ventures, Greycroft, Nvidia, and AMD Ventures, at a valuation of $4.65 billion.

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Wednesday

  1. The Decoder

    Inception Releases Diffusion Language Model Mercury 3, Claiming Speeds Four to Eight Times Faster Than GPT-6 Luna

    Inception has released the diffusion language model Mercury 3, claiming it wins 8 of 12 benchmarks and runs four to eight times faster than GPT-6 Luna and Claude Haiku 5.5. In Inception's own testing, Mercury 3 scored 76.84 on IFBench, 15 points higher than GPT-6 Luna; it scored 51.08 on SWE-Bench Atlas, versus 33.33 and 32.26 for its competitors. Claude Haiku 5.5 led on the DeepSWE long-code test and long-text retrieval. The model generates 1,068 tokens per second on Nvidia GPUs, is priced at $0.20 per million input tokens and $0.70 per million output tokens, with a 50% discount during the launch period, and is now available on the Inception platform as well as OpenRouter, Baseten, and Vercel; there are currently no independent tests.

    higher than GPT-6 Luna; it scored 51.08 on SWE-Bench Atlas, versus 33.33 and 32.26 for its competitors. Claude Haiku 5.5 led on the DeepSWE long-code test and long-text retrieval. The model generates 1,068 tokens per second on Nvidia GPUs, is priced at $0.20 per million input tokens and $0.70 per million output tokens, with a 50% discount during the launch period, and is now available on the Inception platform as well as OpenRouter, Baseten, and Vercel; there are currently no independent tests.

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  2. t3n

    DeepSeek open-sources the Deepinfer inference framework, lowering the cost of self-hosted model deployment

    DeepSeek has open-sourced its internal inference framework Deepinfer on GitHub, with the goal of reducing the compute and energy costs of deploying models on your own. During the inference stage, the framework distributes load more evenly across available processor cores, uses speculative decoding to speed up responses, and improves the structure of results before output; it currently supports only DeepSeek-V4.1-Flash, with more of its own models to be opened up later. On the hardware side, it requires Nvidia GPUs with the SM100 architecture or Huawei Ascend 950 NPUs, so it is not aimed at individual users.

    ute and energy costs of deploying models on your own. During the inference stage, the framework distributes load more evenly across available processor cores, uses speculative decoding to speed up responses, and improves the structure of results before output; it currently supports only DeepSeek-V4.1-Flash, with more of its own models to be opened up later. On the hardware side, it requires Nvidia GPUs with the SM100 architecture or Huawei Ascend 950 NPUs, so it is not aimed at individual users.

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  3. Databricks Blog

    Deploying the RFD4-Proteina model on Databricks

    NVIDIA and Databricks plan to integrate NVIDIA BioNeMo's open-source protein design model RFD4-Proteina into Databricks' Genesis Workbench, running on the now generally available AI Runtime serverless NVIDIA GPU compute. The model can simultaneously generate protein sequences and all-atom structures, and supports optional reward-guided search to steer candidate designs toward target properties such as thermostability and binding affinity. Genesis Workbench includes two built-in RFD4-Proteina workflows, Antibody Design and Vaccine Design, and can be extended through Genie Code and NVIDIA BioNeMo Agent Toolkit skills.

    rfd4-proteina-databricks

    NVIDIA and Databricks plan to integrate NVIDIA BioNeMo's open-source protein design model RFD4-Proteina into Databricks' Genesis Workbench, running on the now generally available AI Runtime serverless NVIDIA GPU compute. The model can simultaneously generate protein sequences and all-atom structures, and supports optional reward-guided search to steer candidate designs toward target properties such as thermostability and binding affinity. Genesis Workbench includes two built-in RFD4-Proteina wor

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  4. Analytics India Magazine

    Vultr to deploy a 20MW AMD MI355X GPU cluster in Chennai

    Cloud infrastructure company Vultr announced it will deploy a 20MW AMD MI355X GPU cluster in Chennai, India, which will be India's largest AMD Instinct GPU deployment and one of the largest in the world. The MI355X was released last June and features 288 GB of HBM3E memory, higher than the 180 GB of Nvidia B200. Vultr operates 34 cloud data center regions in 17 countries worldwide and plans to further expand next year, introducing next-generation MI455X GPUs and the Helios rack-scale AI architecture.

    infrastructure company Vultr announced it will deploy a 20MW AMD MI355X GPU cluster in Chennai, India, which will be India's largest AMD Instinct GPU deployment and one of the largest in the world. The MI355X was released last June and features 288 GB of HBM3E memory, higher than the 180 GB of Nvidia B200. Vultr operates 34 cloud data center regions in 17 countries worldwide and plans to further expand next year, introducing next-generation MI455X GPUs and the Helios rack-scale AI architecture.

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  5. X.PINSelected

    DeepSeek Open-Sources Its Internal Inference Framework DeepInfer

    DeepSeek has open-sourced its internal inference framework DeepInfer, which it says powers the high-throughput, low-latency inference of DeepSeek-V4.1-Flash. The framework supports both Nvidia and Huawei Ascend hardware, and offers capabilities such as distributed inference, speculative decoding, SWA Bounded Replay, and structured output. DeepSeek also released deepseek-recipe, a production-grade tokenization component fully aligned with the official API.

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  6. Tech.eu

    Scaleup Europe Fund Leads $250 Million Funding Round for Pigment

    French enterprise software startup Pigment has completed a $250 million funding round at a valuation of $2.3 billion, led by the Scaleup Europe Fund managed by EQT, with participation from Nvidia and new and existing investors including Greenoaks, Iconiq, IVP, and Meritech. Pigment provides an enterprise business planning platform, and its customers include Siemens, Unilever, Anthropic, and ElevenLabs. The funds from this round will be used to accelerate R&D and expand its engineering team.

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  7. Latent SpaceSelected

    DeepSeek open-sources the DeepInfer inference framework, processing 155 trillion tokens per day in a single cluster

    DeepSeek has open-sourced DeepInfer, its self-developed inference framework that supports DeepSeek-V4.1-Flash, and published production data: a single cluster processes 155 trillion tokens per day, 96% input cache hit rate, 400ms first-token latency, and 230 tokens/s generation speed. The framework is customized based on vLLM and supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, with target platforms NVIDIA SM100 and Huawei Ascend 950. DeepSeek also states that components such as KV cache management and load balancing are not yet fully open-sourced, while the vLLM team says that under a matching GB200 configuration, decoding throughput is within 6% of the results published by DeepInfer.

    latency, and 230 tokens/s generation speed. The framework is customized based on vLLM and supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, with target platforms NVIDIA SM100 and Huawei Ascend 950. DeepSeek also states that components such as KV cache management and load balancing are not yet fully open-sourced, while the vLLM team says that under a matching GB200 configuration, decoding throughput is within 6% of the results published by DeepInfer.

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  8. Latent Space

    DeepSeek open-sources the DeepInfer inference framework, processing 155 trillion tokens per day on a single cluster

    DeepSeek has open-sourced DeepInfer, the production inference framework supporting V4.1 Flash, and published production data including 155 trillion tokens processed per day on a single cluster, a 96% input cache hit rate, an average 400ms time to first token, and 230 tokens/s generation. The framework is customized based on vLLM and uses the Apache 2.0 license, supports distributed inference, speculative decoding, SWA Bounded Replay and structured output, and targets NVIDIA SM100 and Huawei Ascend 950. DeepSeek says components such as KV cache management and load balancing are not fully open-sourced because they are coupled with internal infrastructure, while the vLLM team reports that under matched GB200 configurations, decoding throughput differs from DeepInfer's published results by less than 6%.

    framework is customized based on vLLM and uses the Apache 2.0 license, supports distributed inference, speculative decoding, SWA Bounded Replay and structured output, and targets NVIDIA SM100 and Huawei Ascend 950. DeepSeek says components such as KV cache management and load balancing are not fully open-sourced because they are coupled with internal infrastructure, while the vLLM team reports that under matched GB200 configurations, decoding throughput differs from DeepInfer's published result

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