According to Beating Monitor, Ant Bailing (inclusionAI) has officially released the Ling-3.0-flash weights, offering both the native BF16 version and the FP8 quantized version. Both versions are licensed under MIT and are now available on Hugging Face and ModelScope, allowing deployment via SGLang or vLLM. The BF16 weight file is approximately 255 GB, while the FP8 version is about 128 GB—nearly halving the size. FP8 reduces storage and VRAM requirements by storing parameters with lower precision. In the four official benchmark tests, the maximum performance difference between FP8 and BF16 is 1.57 points. Ling-3.0-flash has a total of 124 billion parameters, activating only 5.1 billion per generation. The model supports a context length of up to 256,000 tokens and is primarily designed for agent tasks such as programming, search, deep research, and tool usage. Official evaluations show that it matches or exceeds the performance of its predecessor, Ring-2.6-1T, which has trillions of parameters, on most benchmarks.
Ant Group open-sources the Ling-3.0-Flash model with 124 billion parameters.
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Summary
Ant Group's InclusionAI has open-sourced the Ling-3.0-flash model under the MIT license, sparking increased interest among developers. The model is available in BF16 (255GB) and FP8 (128GB) versions, with the FP8 variant reducing memory requirements. It contains 124 billion total parameters but uses only 5.1 billion per generation. The model supports a 256,000-token context window and excels in programming and tool calling. The FP8 version shows a maximum performance gap of 1.57 points across four benchmarks compared to BF16. Fear and Greed Index data indicates growing optimism surrounding AI advancements.
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