{
  "title": "How do I train and serve MoE models released in MXFP4 (gpt-oss, DeepSeek-V4)?",
  "summary": "Choose between the convenient QLoRA path, which decodes MXFP4 and re-quantises to NF4, and the native-byte path, which keeps the released blocks and scales in an arena.",
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  "project": "experts4bit-qlora",
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        "purpose": "residency/NVMe/fast-kernel route",
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      "accelerator": [
        "NVIDIA CUDA GPU, sm_80 or newer (Triton kernels via grouped-nf4-gemm)"
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      "special_requirements": [
        "an expert arena baked with grouped-nf4-gemm (nvme_bake_nf4 for NF4, the MXFP4 relocation bake for native MXFP4)",
        "grouped-nf4-gemm>=0.30.0 ([fast] extra; the floor is pyproject.toml's, validated by CI)",
        "local NVMe or a fast block device; pinned host RAM for the hot tier",
        "triton>=3.4 (Linux-only distribution)"
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