# qwen3

Published articles for qwen3.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## MLPerf Inference v6.1: 5.7x Per-Accelerator Gains, a 512-GPU Run, and Vera Rubin's First Peer-Reviewed Numbers

DevFeed: [MLPerf Inference v6.1: 5.7x Per-Accelerator Gains, a 512-GPU Run, and Vera Rubin's First Peer-Reviewed Numbers](<https://devfeed.tech/articles/mlperf-inference-v6-1-5-7x-per-accelerator-gains-a-512-gpu-run-and-vera-rubin-s-first-peer-reviewed-numbers-31404.md>)

Original publisher: [Read original article](<https://www.storagereview.com/news/mlperf-inference-v6-1-5-7x-per-accelerator-gains-a-512-gpu-run-and-vera-rubins-first-peer-reviewed-numbers>)

Author: Harold Fritts

Published: 2026-09-16T15:00:00Z

Content type: news

Language: en

Sources: [StorageReview.com](<https://devfeed.tech/sources/storagereview-com.md>)

Topics: [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [agentic-coding](<https://devfeed.tech/topics/agentic-coding.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Vera Rubin NVL72](<https://devfeed.tech/topics/vera-rubin-nvl72.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Vera Rubin](<https://devfeed.tech/topics/vera-rubin.md>)

Tags: [agentic-coding](<https://devfeed.tech/tags/agentic-coding.md>), [ai](<https://devfeed.tech/tags/ai.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [gpt-oss](<https://devfeed.tech/tags/gpt-oss.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llama-cpp](<https://devfeed.tech/tags/llama-cpp.md>), [numbers](<https://devfeed.tech/tags/numbers.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [qwen3](<https://devfeed.tech/tags/qwen3.md>), [rag](<https://devfeed.tech/tags/rag.md>)

### AI overview

MLCommons published MLPerf Inference v6.1 with record participation, two new inference tests, and peer-reviewed results for several newly covered accelerators. The release reports a 5.7x improvement in the best per-accelerator DeepSeek-R1 server result compared with v5.1.

### Source excerpt

MLCommons has published MLPerf Inference v6.1, and the round sets a participation record with 30 submitting organizations and 486 datacenter and edge results. Two new tests join the suite: an End-to-End RAG pipeline for the datacenter and an Edge Agentic Inference benchmark for single-user devices, and the results carry the first peer-reviewed numbers for NVIDIA's The post MLPerf Inference v6.1: 5.7x Per-Accelerator Gains, a 512-GPU Run, and Vera Rubin's First Peer-Reviewed Numbers appeared first on StorageReview.com.

## Qwen3Guard: следующий шаг в модерации и контроле контента

DevFeed: [Qwen3Guard: следующий шаг в модерации и контроле контента](<https://devfeed.tech/articles/qwen3guard-24031.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/redmadrobot/articles/971388/>)

Author: Martianov (red\_mad\_robot)

Published: 2025-11-28T15:10:54Z

Content type: article

Language: ru

Sources: [Redmadrobot EN](<https://devfeed.tech/sources/redmadrobot-en.md>), [Redmadrobot RU](<https://devfeed.tech/sources/redmadrobot-ru.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [GPU](<https://devfeed.tech/topics/gpu.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [bert](<https://devfeed.tech/tags/bert.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [llm](<https://devfeed.tech/tags/llm.md>), [qwen3](<https://devfeed.tech/tags/qwen3.md>), [rnd](<https://devfeed.tech/tags/rnd.md>), [tag-75a722387627](<https://devfeed.tech/tags/tag-75a722387627.md>), [tag-75dea8ea04f0](<https://devfeed.tech/tags/tag-75dea8ea04f0.md>), [tag-a6ed1cca9b15](<https://devfeed.tech/tags/tag-a6ed1cca9b15.md>)

### AI overview

The article discusses building content moderation for open-text services. It compares using an LLM as a moderator with an embedding-based classifier trained on 40,000 labeled query examples, reporting latency of about 20 ms per request instead of 700-900 ms, while noting that the simpler model struggles with context, irony, hints, and jailbreaks.

### Source excerpt

Всем привет! Меня зовут Миша Мартьянов, я инженер по исследованиям и разработке в лаборатории AI R&D в red_mad_robot. В мои задачи входит проверка гипотез и развитие наших продуктов. Однако недостаточно просто улучшать продукты, необходимо также чтобы они работали устойчиво и безопасно. Ранее я рассказывал разработку идеального контент-фильтра на базе Guardrails. Но время не стоит на месте: появляются новые модели и новые практики их применения. Этому и будет посвящён наш сегодняшний разговор. Читать далее

## Scaleway on Hugging Face Inference Providers 🔥

DevFeed: [Scaleway on Hugging Face Inference Providers 🔥](<https://devfeed.tech/articles/scaleway-on-hugging-face-inference-providers-7284.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/inference-providers-scaleway>)

Author: Guillaume Noale; Franck Pagny; Fred Bardolle; Guillaume Calmettes; Constance Morales; Célina Hanouti; Julien Chaumond; Simon Brandeis; Lucain Pouget

Published: 2025-09-19T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [scaleway](<https://devfeed.tech/topics/scaleway.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [inference-providers](<https://devfeed.tech/topics/inference-providers.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>), [gpt-oss](<https://devfeed.tech/topics/gpt-oss.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [API keys](<https://devfeed.tech/topics/api-keys.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Sovereign AI](<https://devfeed.tech/topics/sovereign-ai.md>)

Tags: [api-keys](<https://devfeed.tech/tags/api-keys.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [gemma](<https://devfeed.tech/tags/gemma.md>), [gpt-oss](<https://devfeed.tech/tags/gpt-oss.md>), [hub](<https://devfeed.tech/tags/hub.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-providers](<https://devfeed.tech/tags/inference-providers.md>), [llms](<https://devfeed.tech/tags/llms.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>), [providers](<https://devfeed.tech/tags/providers.md>), [qwen3](<https://devfeed.tech/tags/qwen3.md>), [scaleway](<https://devfeed.tech/tags/scaleway.md>), [sdks](<https://devfeed.tech/tags/sdks.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

Hugging Face announces Scaleway as a supported Inference Provider on the Hugging Face Hub. The integration provides serverless access to open-weight and frontier AI models through model pages, client SDKs, and APIs, with European data centers, pay-per-token pricing, low latency, and production-oriented features.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Evaluating LLMs for my personal use case

DevFeed: [Evaluating LLMs for my personal use case](<https://devfeed.tech/articles/evaluating-llms-for-my-personal-use-case-35441.md>)

Original publisher: [Read original article](<https://darkcoding.net/software/personal-ai-evals-aug-2025/>)

Author: Graham King

Published: 2025-08-23T17:00:00Z

Content type: opinion

Language: en

Sources: [Graham King](<https://devfeed.tech/sources/graham-king.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Programming](<https://devfeed.tech/topics/programming.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Rust](<https://devfeed.tech/topics/rust.md>), [Command-line interface](<https://devfeed.tech/topics/cli.md>), [Linux](<https://devfeed.tech/topics/linux.md>), [Python](<https://devfeed.tech/topics/python.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [coding](<https://devfeed.tech/tags/coding.md>), [devstral](<https://devfeed.tech/tags/devstral.md>), [evals](<https://devfeed.tech/tags/evals.md>), [latency](<https://devfeed.tech/tags/latency.md>), [linux](<https://devfeed.tech/tags/linux.md>), [llms](<https://devfeed.tech/tags/llms.md>), [programming](<https://devfeed.tech/tags/programming.md>), [python](<https://devfeed.tech/tags/python.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [qwen3](<https://devfeed.tech/tags/qwen3.md>), [software](<https://devfeed.tech/tags/software.md>)

### AI overview

The author evaluates a set of language models against 130 real prompts drawn from personal bash history, covering programming, system administration, technical explanations, general knowledge, and creative tasks. The evaluation uses blinded Rust scripts and records cost, latency, and throughput, with models selected based on prior experience, leaderboards, and price.

### Source excerpt

My life is not a math Olympiad

## Kimina-Prover: Applying Test-time RL Search on Large Formal Reasoning Models

DevFeed: [Kimina-Prover: Applying Test-time RL Search on Large Formal Reasoning Models](<https://devfeed.tech/articles/kimina-prover-applying-test-time-rl-search-on-large-formal-reasoning-models-6978.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/AI-MO/kimina-prover>)

Author: Haiming Wang; Mert Unsal; Xiaohan Lin; MantasBaksys; Junqi Liu; Marco Dos Santos; Flood Sung; Ying; Zhu Zekai; Lujianqiao; Hugues de Saxcé; Thibaut Barroyer; Ebony Zhang; Bolton Bailey; Frederick Pu;

Published: 2025-07-10T12:54:19Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Lean](<https://devfeed.tech/topics/lean.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [large-language-model](<https://devfeed.tech/tags/large-language-model.md>), [qwen3](<https://devfeed.tech/tags/qwen3.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [rl](<https://devfeed.tech/tags/rl.md>), [test](<https://devfeed.tech/tags/test.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

The article announces Kimina-Prover-72B, a theorem-proving model for Lean 4, along with distilled 8B and 1.7B variants. It describes test-time reinforcement-learning search, recursive lemma composition, and error-directed proof repair. The model achieves a 92.2% pass rate on the miniF2F benchmark.

### Source excerpt

Numina & Kimi Team We're excited to announce the release of Kimina-Prover-72B, our state-of-the-art theorem proving model trained with the Kimi k1.5[1] RL pipeline based on Qwen2.5-72B [2]. Alongside it, we are also releasing two distilled variants: Kimina-Prover-Distill-8B and 1.7B (based on Qwen3-8B and Qwen3-1.7B[3] respectively).

## Improving Hugging Face Model Access for Kaggle Users

DevFeed: [Improving Hugging Face Model Access for Kaggle Users](<https://devfeed.tech/articles/improving-hugging-face-model-access-for-kaggle-users-7299.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/kaggle-integration>)

Author: Vincent Roseberry; Meg Risdal; Julien Chaumond; Pedro Cuenca; Vaibhav Srivastav

Published: 2025-05-14T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Kaggle](<https://devfeed.tech/topics/kaggle.md>), [qwen](<https://devfeed.tech/topics/qwen.md>), [spaces](<https://devfeed.tech/topics/spaces.md>)

Tags: [add-ons](<https://devfeed.tech/tags/add-ons.md>), [announcement](<https://devfeed.tech/tags/announcement.md>), [code](<https://devfeed.tech/tags/code.md>), [community](<https://devfeed.tech/tags/community.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [hub](<https://devfeed.tech/tags/hub.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [kaggle](<https://devfeed.tech/tags/kaggle.md>), [model](<https://devfeed.tech/tags/model.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [qwen3](<https://devfeed.tech/tags/qwen3.md>), [secrets](<https://devfeed.tech/tags/secrets.md>), [spaces](<https://devfeed.tech/tags/spaces.md>), [token](<https://devfeed.tech/tags/token.md>)

### AI overview

Kaggle is integrating Hugging Face models into its platform, improving model discovery, navigation, and reuse in Kaggle Notebooks. Public notebooks using Hugging Face models can contribute code examples to Kaggle model pages, while private and gated models continue to require Hugging Face authentication. Support for offline Kaggle competition submissions is still in development.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.