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Z.ai argues that model scaling should account for data, compute allocation, inference cost, sparsity, effective depth, and post-training—not parameters alone. The post presents GLM-5.3 as a controlled experiment using the same total and activated parameters as GLM-5.2 while scaling long-horizon environments and reinforcement learning for one month.
Ypsilanti Township has temporarily blocked the electrical substation needed by a University of Michigan data center intended for nuclear-weapons research. The township also imposed a one-year moratorium on the facility’s requested water connection, adding local infrastructure barriers to the 220,000-square-foot project.
Loudoun County has changed its zoning rules so new data centers can no longer be approved as routine office-park projects. Developers must now obtain approval from residents and local government, ending more than 25 years of relatively easy approvals.
A builder reports running DeepSeek V4 Flash-0731 at roughly 130–150 tokens per second using 16 RTX 5060 Ti 16GB GPUs connected through two PLX PEX88096 switch islands. The configuration supports up to 500,000-token context with tensor parallelism 8 and up to 1 million tokens with tensor parallelism 4.
Aurora-80K is a tiny language model with exactly 80,000 parameters and a factorized 4,096-token vocabulary. Its reported results include 3.2902 Wikitext-2 BPB, 52.31% BLiMP accuracy, and 26.05% on ARC-Easy.
Big Tech companies are increasing local public-relations efforts to win support for the data centres needed to expand AI infrastructure. Campaigns reportedly include town halls, tax concessions, and sponsorship of community groups as opposition grows across the United States.
GP practices in Rotherham have introduced Emma, an AI receptionist that reportedly supports 17 languages. Healthwatch Rotherham says some patients are frustrated because the system struggles to understand broad local accents, causing callers to hang up.
A community test on an AMD R9700 with ROCm reports noticeable quality and long-context retention differences between FP16 and q8_0 KV cache for Qwen3.8-27B. FP16 reportedly produces more careful structured output and maintains performance beyond 120k tokens, challenging the assumption that both formats are equivalent.
A Reddit user reports that Qwen3.8-27B performs substantially worse than Qwen3.6 on obscure trivia and offline knowledge tests. The model may still be strong for coding and tool-assisted workflows, but users relying on its internal weights for broad factual recall should be cautious.
A Reddit user reports that Qwen3.8-27B autonomously retrieved a university class schedule through 80 tool calls using only credentials and a university name. In another test, it downloaded and analyzed a social-media video, installed Whisper for transcription, and enhanced video frames without human intervention.