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News/Training an MoE LLM in MXFP4: How We Closed the Gap to BF16
arxiv
PublishedOctober 27, 2026 at 4:00 AM

Training an MoE LLM in MXFP4: How We Closed the Gap to BF16

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arXiv:2610.32229v2 Announce Type: replace Abstract: Training large language models (LLMs) in sub-8-bit arithmetic promises substantial gains in memory bandwidth and compute throughput, yet FP4 formats introduce quantization error severe enough to destabilize optimization if left unaddressed. We pres

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