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TensorFold

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Headlines · 3

  1. M5 Ultra Mac Studio beats a single GB10 in agentic test but trails a Spark pair

    Tom's Hardware benchmarked an M5 Ultra Mac Studio with a 36-core CPU, 80-core GPU and up to 512GB of unified memory against a single Dell Pro Precision GB10 system, which it described as nearly identical to an Nvidia DGX Spark. Using llama.cpp with Qwen 3.8 27B and its native multi-token prediction, the Mac completed the Artificial Analysis AgentPerf-Local test in just under half the time of the GB10 system. The report also cited 1.2 TB/s of memory bandwidth for the Mac versus 273 GB/s for the GB10. For DeepSeek V4.1 Flash 1017, however, the Mac's llama.cpp setup took 10–25 minutes for the longest tested contexts, while a two-Spark cluster using DSpark speculative decoding and TensorFold took under five minutes. The report said Mac Studio lead times stretched into February 2027.

    Tom’s Hardware · 🔥 7
  2. Two 256GB M5U systems reportedly cluster worse than running separate models

    A Reddit user reports that clustering two 256GB M5U systems produced worse results than running two models separately, citing concurrency issues and inconsistent decode-speed gains. The poster currently runs Qwen Flash Next q6 and GLM3.5 Flash q4 locally, while using DeepSeek through an API, and says GLM q4 outperformed the API for their coding work.

    r/LocalLLaMA · 🔥 0
  3. Reddit user reports GLM and Qwen results on a dual-M5U cluster

    A Reddit user reported running GLM 5.3 q6 and Qwen Flash Next q6 on two 256GB M5U units with 80 cores each. The post claims 4.9k prefill and 50 tokens per second for GLM, while Qwen reached 8k prefill and 200 tokens per second through Tensorfold; the author explicitly questioned the GLM prefill figure because it came from an LLM's explanation. The author said DeepSeek configured oMLX from guides and asked the community for optimization advice.

    r/LocalLLaMA · 🔥 0

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