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ARXIV:2603.02731 · MOE MODEL TRAINING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.02731MOE MODEL TRAININGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Optimize large-scale MoE model training on Hopper GPUs by implementing FP4 without native support.
Opportunity summary
Pain Optimize large-scale MoE model training on Hopper GPUs by implementing FP4 without native support.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Optimize large-scale MoE model training on Hopper GPUs by implementing FP4 without native support. In this work, we present a training recipe that enables MXFP4 efficiency for MoE models on Hopper architectures without native…
Training large-scale Mixture-of-Experts (MoE) models is bottlenecked by activation memory and expert-parallel communication, yet FP4 training remains impractical on Hopper-class GPUs without native MXFP4 or NVFP4 support. In this work, we present a training…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Training large-scale Mixture-of-Experts (MoE) models is bottlenecked by activation memory and expert-parallel communication, yet FP4 training remains impractical on Hopper-class GPUs without native MXFP4…
MoE Model Training moved forward this cycle; last verified April 2026. Public score 3.0/10.
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Optimize large-scale MoE model training on Hopper GPUs by implementing FP4 without native support.
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Paper Pack
10.48550/arXiv.2603.02731Optimize large-scale MoE model training on Hopper GPUs by implementing FP4 without native support.
Abstract
Training large-scale Mixture-of-Experts (MoE) models is bottlenecked by activation memory and expert-parallel communication, yet FP4 training remains impractical on Hopper-class GPUs without native MXFP4 or NVFP4 support. In this work, we present a training recipe that enables MXFP4 efficiency for MoE models on Hopper architectures without native 4-bit computation support. A central challenge is to integrate FP4 into an existing BF16/FP8 hybrid training pipeline without incurring costly precision round-trips (e.g., FP4 $\leftrightarrow$ BF16 $\leftrightarrow$ FP8). We address this challenge by introducing direct FP8-to-FP4 quantization and de-quantization, together with scaling-aware FP4 row-wise to column-wise conversion, enabling FP4 activations and expert-parallel communication with minimal overhead. Core MoE computations are executed in FP8, while activations and expert-parallel communication are compressed using MXFP4, achieving substantial memory and bandwidth savings without degrading convergence. At the 671B parameter scale, our method achieves end-to-end training performance comparable to strong FP8 baselines, while reducing peak activation memory by 14.8\% (11.8 GB) and improving training throughput by 12.5\%, from 1157 to 1302 tokens per GPU per second. These results show that FP4 efficiency can be practically realized for large-scale MoE training through careful software-hardware co-design, even without native FP4 Tensor Core support.
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unverified0 refs; 0 sources; 17% coverage.
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Dimensions overall score 3.0
PROBLEM
Optimize large-scale MoE model training on Hopper GPUs by implementing FP4 without native support. In this work, we present a training recipe that enables MXFP4 efficiency for MoE models on Hopper architectures without native 4-bit computation support.
METHOD
Training large-scale Mixture-of-Experts (MoE) models is bottlenecked by activation memory and expert-parallel communication, yet FP4 training remains impractical on Hopper-class GPUs without native MXFP4 or NVFP4 support. In this work, we present a training recipe that enables M...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Training large-scale Mixture-of-Experts (MoE) models is bottlenecked by activation memory and expert-parallel communication, yet FP4 training remains impractical on Hopper-class GPUs without native MXFP4...
WHY NOW
MoE Model Training moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Optimize large-scale MoE model training on Hopper GPUs by implementing FP4 without native support. In this work, we present a training recipe that enables MXFP4 efficiency for MoE models on Hopper architectures without native 4-bit computation support.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Training large-scale Mixture-of-Experts (MoE) models is bottlenecked by activation memory and expert-parallel communication, yet FP4 training remains impractical on Hopper-class GPUs without native MXFP4 or NVFP4 support. In this work, we present a training recipe that enables MXFP4 efficiency for MoE models on Hopper architectures without native 4-bit computation support.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Training large-scale Mixture-of-Experts (MoE) models is bottlenecked by activation memory and expert-parallel communication, yet FP4 training remains impractical on Hopper-class GPUs without native MXFP4 or NVFP4 support.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
MoE Model Training moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Optimize large-scale MoE model training on Hopper GPUs by implementing FP4 without native support.
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MoE Model Training
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Commercial read
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