Most Core LLaMA Creators Resign, Inside Story of Meta AI Exposed! Power Struggle Turns Hostile as Team Members Change Three Times

AI Code Model Open-Source Boom: Cogito v1 Preview Unveiled, 70B Parameter Model Outperforms Llama 4

Recently, the AI code generation field has seen a surge in open-source releases, with several heavyweight models making their debut. Among them, the Cogito v1 Preview series from Deep Cogito stands out. According to AIbase, this new family of open-source models includes various sizes: 3B, 8B, 14B, 32B, and 70B parameters. Not only does it outperform competitors in its class, but its 70B version even surpasses Meta's recently released Llama 4 109B MoE model, sparking considerable industry discussion.

Llama 4 Arrives on Vertex AI: Deploy Meta's New Model with One Click

Google Cloud Platform recently announced that Meta's latest generation of open-source large language models, Llama 4, is now available in its Vertex AI Model Garden. The news has generated significant excitement in the global tech community. The Scout and Maverick models from the Llama 4 series are now integrated into Vertex AI and available to developers via fully managed Model-as-a-Service (MaaS) API endpoints in preview.

NVIDIA Unveils Llama 3.1 Nemotron Ultra 253B, Outperforming Llama 4 Behemoth

NVIDIA Unveils Llama 3.1 Nemotron Ultra 253B: Redefining AI Performance Standards

NVIDIA, a global leader in chip and AI technology, recently launched a groundbreaking new open-source large language model, Llama 3.1 Nemotron Ultra 253B, generating significant excitement within the AI community. Built upon Meta's Llama-3.1-405B, this model boasts innovative optimizations that surpass competitors like Llama 4 Behemoth and Maverick in performance, while demonstrating superior resource efficiency and exceptional multi-tasking capabilities.

NVIDIA Unveils Llama 3.1 Nemotron Ultra 253B: A New Benchmark in Performance

On April 8th, 2025, NVIDIA launched Llama 3.1 Nemotron Ultra 253B, an open-source model optimized from Llama-3.1-405B. With 25.3 billion parameters, it surpasses Meta's Llama 4 Behemoth and Maverick, becoming a focal point in the AI field. This model demonstrates superior performance in benchmarks such as GPQA-Diamond, AIME 2024/25, and LiveCodeBench, achieving inference throughput comparable to DeepSeek.

Meta Executive Denies Allegations of Cheating in Llama 4 Training

Meta's Llama 4 Makes a Strong Debut, But Stumbles on Long-Context Tasks

Meta Releases Llama 4 Large Language Model: Mixed-Expert Architecture Ushers in a New Era for AI

Meta has released its latest open-source AI model, Llama 4, marking another significant advancement in the field of artificial intelligence. Llama 4 comes in two versions, Scout and Maverick, designed to enhance AI model capabilities and performance. Meta states that Llama 4 is a multimodal large language model capable of processing various data types, including text, images, video, and audio, and can freely convert between these formats. Notably, the Llama 4 series is the first...

Meta Accused of AI Model Double Standard: Maverick's Performance Varies Widely Between Evaluation and Public Versions

Meta released its new flagship AI model, Maverick, on Saturday. The model ranked second in the LM Arena benchmark. LM Arena is a testing platform that relies on human raters to compare different model outputs and select their preferences. However, several AI researchers quickly discovered that the version of Maverick deployed to LM Arena appears significantly different from the version widely used by developers. Meta acknowledged in its announcement that the Maverick on LM Arena is an experimental version.

Meta Announces Open-Source Multimodal Llama 4 with Impressive Performance

Meta, the American tech giant, has released Llama 4, its most powerful open-source AI model. The initial release includes two models: Llama4Scout and Llama4Maverick. Llama4Scout boasts 109 billion parameters, 17 billion active parameters, and 16 experts. Its key feature is support for a 10 million context window, enabling processing of over 20 hours of video, all while running on a single H100 GPU (after Int4 quantization).