Qwen3-235B

Headlines · 2

1. 4d
   Exowatt builds reusable SageMaker fine-tuning pipeline

   AWS Machine Learning Blog reports that Exowatt used Amazon SageMaker AI serverless model customization to create a repeatable pipeline for task-specific fine-tuned models. A fine-tuned Qwen3-1.7B handled offline alarm triage, while a fine-tuned Qwen3-14B supported cloud energy-market analysis; AWS claims higher accuracy than frontier models at up to 49× lower cost, though those results were not independently verified here. The pipeline turns operational data into a certified training set, evaluated model and deployment artifact, reducing Exowatt's reported workflow from months to days.

   AWS Machine Learning Blog · 🔥 0
2. Oct 12
   AirLLM claims 2.8T-parameter Kimi K3 runs on a single 4GB GPU, adds low-VRAM training

   AirLLM is an open-source inference and training library that streams weights one layer at a time, keeping only a single layer resident on the GPU to cut VRAM use sharply. According to its repository, it runs 70B models on a single 4GB card without quantization, distillation or pruning, and reports running Kimi K3 (2.8T) in 3.72GB, Qwen3.8-Flash-Next (125B) in about 5.95GB and DeepSeek-V3 (671B) in roughly 12GB. Recent updates also add small-VRAM training, fine-tuning Qwen3.8-Flash-Next (125B) under 6GB and Qwen3.8-27B at about 2GB. All VRAM and training figures are the project's own reported measurements and have not been independently verified.

   GitHub Trending（每日） · 🔥 0

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