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  1. Story1 reports · 1 facts

    Ant Ling officially open-sources Ling-3.0-tiny

    On August 11, 2026, Ant Ling officially open-sourced Ling-3.0-tiny. According to the official introduction, this is a natively hybrid reasoning model with 7.9B total parameters, activating only 1.3B parameters during inference, and it is simultaneously offered in three versions: BF16, FP8, and INT4. The company states that it scores 25 on the Artificial Analysis Intelligence Index and 16 on the Agentic Index, with an output speed exceeding 160 tokens/s. In terms of deployment, the model can perform FP8 inference on a single NVIDIA DGX Spark, with weights of approximately 7.85GB, and has also been adapted for local operation on Apple Silicon MacBook and Mac mini. The model is currently in an open-source release state, with no further developments at this time.

    versions: BF16, FP8, and INT4. The company states that it scores 25 on the Artificial Analysis Intelligence Index and 16 on the Agentic Index, with an output speed exceeding 160 tokens/s. In terms of deployment, the model can perform FP8 inference on a single NVIDIA DGX Spark, with weights of approximately 7.85GB, and has also been adapted for local operation on Apple Silicon MacBook and Mac mini. The model is currently in an open-source release state, with no further developments at this time.

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  2. Story1 reports · 1 facts

    Ant Ling officially open-sources the Ling-3.0-flash model

    On August 7, 2026, Ant Ling officially open-sourced Ling-3.0-flash, a new-generation native hybrid reasoning model. According to the Ant Ling developer blog, the model uses a MoE architecture with 124B total parameters and 5.1B activated parameters, and is released alongside a base version as well as multiple precision versions including FP8, FP4, and INT4. There are three deployment options: cloud API calls; end-to-end local inference on a single NVIDIA DGX Spark using the MXFP4 or INT4 version; and a high-performance deployment that exceeds an average output rate of 1,100 tokens/s under the specified GPU test configuration. In terms of benchmark data, its output speed in Artificial Analysis is 353 tokens/s, and in the AA Intelligence Index its weighted average invocation cost per task is about $0.04. There are currently no subsequent development facts.

    ide a base version as well as multiple precision versions including FP8, FP4, and INT4. There are three deployment options: cloud API calls; end-to-end local inference on a single NVIDIA DGX Spark using the MXFP4 or INT4 version; and a high-performance deployment that exceeds an average output rate of 1,100 tokens/s under the specified GPU test configuration. In terms of benchmark data, its output speed in Artificial Analysis is 353 tokens/s, and in the AA Intelligence Index its weighted average

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