←Search results

Search “DeepSeek”

By relevance · Total 1375 results Clear search

Wednesday

  7. Huxiu

    Three major Chinese tech companies order over 3 million NVIDIA RTX PRO 5500 cards, with order value exceeding $35 billion

    According to Reuters, three major Chinese internet companies have collectively ordered more than 3 million NVIDIA RTX PRO 5500 workstation graphics cards. At a list price of about $11,400, the potential value exceeds $35 billion. NVIDIA has told partners that global production of the card in 2027 may not exceed 3 million units, and actual deliveries may be only 5% to 10%. The card uses the same GB202 chip as the RTX 5090, with memory increased from 32GB to 84GB, bandwidth reduced from about 1792GB/s to 1398GB/s, and support for MIG partitioning; it is mainly aimed at AI inference. Total orders from Chinese customers have exceeded 6 million units. The major companies placed orders early to secure priority supply given limited GB202 production capacity.

    According to Reuters, three major Chinese internet companies have collectively ordered more than 3 million NVIDIA RTX PRO 5500 workstation graphics cards. At a list price of about $11,400, the potential value exceeds $35 billion. NVIDIA has told partners that global production of the card in 2027 may not exceed 3 million units, and actual deliveries may be only 5% to 10%. The card uses the same GB202 chip as the RTX 5090, with memory increased from 32GB to 84GB, bandwidth reduced from about 1792

    Matched fieldsBody

    #hot-signal#Industry news#English text is being prepared.#English text is being prepared.
  12. Titanium Media · AGISelected

    MiniMax Releases M3.1 Flash, Tested Frontend Coding Capabilities

    MiniMax claimed the anonymous model Space Bunny on October 19 and officially released M3.1 Flash, which uses a sparse attention architecture with 428B total parameters and about 23B activated, nearly 3x faster inference, 70% lower usage cost, and pricing of 1 yuan per million input tokens, 4 yuan for output, and 0.1 yuan for cache reads. Model weights will be open-sourced on October 23, and the official announcement previews a Pro version with a higher intelligence ceiling coming soon. The author replaced the community-viral cases with four new questions of the same type for retesting, and believes its frontend Coding capabilities improved noticeably while retaining the self-verification behavior of proactively opening a browser to trial and error.

    MiniMax claimed the anonymous model Space Bunny on October 19 and officially released M3.1 Flash, which uses a sparse attention architecture with 428B total parameters and about 23B activated, nearly 3x faster inference, 70% lower usage cost, and pricing of 1 yuan per million input tokens, 4 yuan for output, and 0.1 yuan for cache reads. Model weights will be open-sourced on October 23, and the official announcement previews a Pro version with a higher intelligence ceiling coming soon. The autho

    Matched fieldsBody

    #hot-signal#Model releases#English text is being prepared.#Open-source ecosystem

Tuesday

  1. QbitAI

    MiniMax Releases M3.1 Flash Model, ALE Score of 29.6% Ranks First Among Domestic Models

    MiniMax officially released M3.1 Flash, formerly known as Space Bunny (Yutu), which previously topped the charts anonymously on OpenRouter. The model scores 29.6% on Agents' Last Exam, ranking first among domestic models, and scores 43 on the AA leaderboard; it achieves 73.7% on DeepSWE v1.1, 82.2 points on CyberGym, 76.2 points on BankerToolBench, tying for first with Claude Opus 5, and 61.7 points on OSWorld V2. The model adopts a sparse attention architecture, with output speeds of up to 200+ TPS and a median of 170+ TPS, input at 1 yuan per million tokens, output at 4 yuan, and cache reads at 0.1 yuan, with comprehensive usage costs reduced by 70% compared to the previous generation M3.

    MiniMax officially released M3.1 Flash, formerly known as Space Bunny (Yutu), which previously topped the charts anonymously on OpenRouter. The model scores 29.6% on Agents' Last Exam, ranking first among domestic models, and scores 43 on the AA leaderboard; it achieves 73.7% on DeepSWE v1.1, 82.2 points on CyberGym, 76.2 points on BankerToolBench, tying for first with Claude Opus 5, and 61.7 points on OSWorld V2. The model adopts a sparse attention architecture, with output speeds of up to 200+

    Matched fieldsBody

    #chinese#Model releases#Agents#English text is being prepared.