Model Detail
MiniMax-M3-NVFP4
▲ 9.1%MiniMax-M3-NVFP4 is a code generation model with 123.3B parameters released by NVIDIA. The model is registered under the text-generation pipeline tag on Hugging Face, distributed under a other license.
MiniMax-M3-NVFP4 ships with 123.3B parameters. Total weight footprint is approximately 246.6 GB, which is the relevant figure when planning local-inference VRAM. Distribution is governed by the other license — review the exact terms before commercial deployment.
Downloads of MiniMax-M3-NVFP4 have moved +9.1% over the past 24 hours, +21912.4% over the trailing seven days. That puts the model in active uptrend territory; a sustained move of this size usually reflects a recent release, a viral integration, or a benchmark surprise rather than steady-state demand. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.
MiniMax-M3-NVFP4 is best fit for code completion, repository-scale Q&A, and pair-programming integrations. It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Honest Physical-Support Inference after Latent Dictionary Learning: Collision Singularities and Minimax Resolution
arXiv:2607.16813v1 Announce Type: new Abstract: Sparse-support uncertainty is usually quantified by treating the dictionary as known, an assumption that can produce overconfident, label-dependent conclusions when the dictionary is learned from latent sparse mixtures. Near collisions of coherent atom
Minimax and Bayes Optimal Best-Arm Identification
arXiv:2506.24007v5 Announce Type: replace-cross Abstract: This study investigates minimax and Bayes optimal strategies for fixed-budget best-arm identification. We consider an adaptive procedure consisting of a sampling phase followed by a recommendation phase, and we design an adaptive experiment w
Bandit PCA with Minimax Optimal Regret
arXiv:2607.10936v2 Announce Type: replace Abstract: We study the bandit-feedback version of online principal component analysis (Bandit PCA): in each round $t = 1,\dots,T$, the adversary selects a $d \times d$ symmetric gain matrix $G_t$ with spectrum in $[0,1]$ and rank at most $r$; the learner sim
Price of Fairness in Bandits: A Tight Minimax Characterization
arXiv:2607.13402v1 Announce Type: cross Abstract: In bandit problems, standard regret-minimizing algorithms treat exploration as an amortized cost, which can expose early participants to unfair ex-ante losses in settings such as clinical trials. Recent work addresses this by evaluating the sequence
Bilateral Trade Under Heavy-Tailed Valuations: Minimax Regret with Infinite Variance
arXiv:2603.06851v2 Announce Type: replace-cross Abstract: We study contextual bilateral trade under full feedback when, conditionally on the context, trader valuations have bounded density but infinite variance. We first extend the self-bounding property of Bachoc et al. (ICML 2025) from bounded to