Llama 3.1 405B

Headlines · 2

1. 3d
   China’s frontier AI training constrained by compute gaps

   Epoch AI data cited by 36Kr says Chinese frontier models have averaged about seven months behind US models since 2023, while Stanford University’s 2026 AI Index reports that the performance gap has narrowed to about 2.7%. Epoch AI estimates put DeepSeek-R1 training compute at about 4×10^24 floating-point operations, versus roughly 6.6×10^25 for GPT-5 and 5×10^26 for Grok 4. The analysis examines China’s constraints in high-end GPUs, HBM, networking, software, power and cluster utilization, and recommends system engineering and model efficiency in the short term, capacity expansion and large-scale validation in the medium term, and domestic equipment and talent development in the long term.

   36氪 人工智能 · 🔥 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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