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Oct 14

Oct 14Wed
  1. Databricks BlogOfficialAI score67

    Databricks cuts Qwen 3.8 27B fine-tuning time from 7h44m to 1h17m

    AIDatabricks reports cutting fine-tuning time for Qwen 3.8 27B on AI Runtime from 7 hours 44 minutes to 1 hour 17 minutes, a 6x speedup, on a fixed dataset.

    Why it matters: The post breaks down each step that cut Qwen 3.8 27B fine-tuning time from 7h44m to 1h17m, showing which optimizations transfer to other training jobs.

  2. Artificial AnalysisOfficialAI score39
    Image from @ArtificialAnlys's post

    AMD MI355X beats Nvidia B200 on DeepSeek V4 Pro throughput per MW

    AIArtificial Analysis finds AMD's MI355X delivers 15% to 40% more throughput per MW than Nvidia's B200 on DeepSeek V4 Pro 0813 between 35 and 75 tok/s per user, peaking at about 94M tokens/s per MW at 33 tok/s per user.

  3. Artificial AnalysisOfficialAI score38
    Image from @ArtificialAnlys's post

    Kimi K3 serves 8M tokens/s per MW on MI355X, about a third of B300

    AIArtificial Analysis says Kimi K3, with 2.8T total and 104B active parameters, serves about 8M tokens/s per MW on AMD MI355X at 50 tok/s per user, roughly a third of the roughly 24M reached on NVIDIA B300 with multi-node disaggregated configs.

  4. Artificial AnalysisOfficialAI score38
    Image from @ArtificialAnlys's post

    AMD MI355X beats Nvidia B200 on DeepSeek V4 Pro throughput per MW

    AIArtificial Analysis finds AMD's MI355X delivers 15% to 40% more throughput per MW than Nvidia's B200 on DeepSeek V4 Pro 0813 between 35 and 75 tok/s per user, peaking at about 94M tokens/s per MW at 33 tok/s per user.

  5. Artificial AnalysisOfficialAI score40
    Image from @ArtificialAnlys's post

    Kimi K3 serves 8M tokens/s per MW on MI355X, about a third of B300

    AIArtificial Analysis says Kimi K3, with 2.8T total and 104B active parameters, serves about 8M tokens/s per MW on AMD MI355X at 50 tok/s per user, roughly a third of the roughly 24M reached on NVIDIA B300 with multi-node disaggregated configs.