PulseAugur
中
实时 16:49:09
English(EN) DeepSeek V4 Complete Guide — 1.6T MoE with 1M Context at 73% Lower Cost

DeepSeek V4 发布，拥有 1.6T MoE、1M 上下文和更低成本

作者 PulseAugur 编辑部 · [4 个来源] ·

DeepSeek V4 是一个开放权重模型系列，已发布，采用 1.6 万亿参数的专家混合（MoE）架构，每个 token 只激活 490 亿参数。该新模型拥有 100 万 token 的上下文窗口，并显著降低了推理成本，由于混合注意力（Hybrid Attention）等创新，成本比前代产品降低高达 73%。V4 系列可在 Hugging Face 上获取，其质量可与 GPT-5.4 和 Claude Opus 4.6 等领先模型相媲美，但价格却低得多，并且针对 NVIDIA Blackwell 进行了硬件性能优化。 AI

影响 为大型 MoE 模型树立了新的效率标准，使开发人员能够更轻松、更经济地获得先进的 AI 功能。

排序理由 DeepSeek（一家重要的 AI 实验室）发布了新模型，并提供了详细的技术规格和基准测试比较。

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →

DeepSeek V4 发布，拥有 1.6T MoE、1M 上下文和更低成本

报道来源 [4]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    How to Self Host DeepSeek V4 on Bare Metal GPUs Reclaim data sovereignty and escape the API tax. Deploying massive MoE models requires exact engineering: 158GB

    How to Self Host DeepSeek V4 on Bare Metal GPUs Reclaim data sovereignty and escape the API tax. Deploying massive MoE models requires exact engineering: 158GB (FP8 weights) + 10GB (1M token KV Cache) = 168GB VRAM required. A 4x NVIDIA L40S ServerMO cluster provides 192GB headroo…

    链接 servermo.com/…/self-host-deepseek-v4-bare…
  2. dev.to — LLM tag TIER_1 English(EN) · Jenny Met ·

    DeepSeek V4 Complete Guide — 1.6T MoE with 1M Context at 73% Lower Cost

    <h1> DeepSeek V4 Complete Guide — 1.6T MoE with 1M Context at 73% Lower Cost </h1> <p>DeepSeek V4 dropped on April 24, 2026, and it's the most efficient open-weight model family we've seen. A 1.6-trillion-parameter Mixture-of-Experts architecture that only activates 49 billion pa…

  3. Mastodon — mastodon.social TIER_1 English(EN) · aihaberleri ·

    📰 DeepSeek V4 Compressed Attention Reduces KV-Cache Memory by 98% DeepSeek V4's revolutionary compressed attention architecture dramatically reduces KV-cache me

    📰 DeepSeek V4 Compressed Attention Reduces KV-Cache Memory by 98% DeepSeek V4's revolutionary compressed attention architecture dramatically reduces KV-cache memory requirements while maintaining a 1 million-token context window. The innovative approach compresses along the seque…

    链接 aihaberleri.org/…/deepseek-v4-compressed-…
  4. Mastodon — mastodon.social TIER_1 Türkçe(TR) · aihaberleri ·

    📰 DeepSeek V4 2026: KV Cache Reduced to 2% with LLM Architecture Revolution, 1M Token Success DeepSeek V4, only 2% KV cache for a 1 million token context window

    📰 DeepSeek V4 2026: LLM Mimarisi Devrimi ile KV Cache %2'ye Düştü, 1M Token Başarısı DeepSeek V4, 1 milyon tokenlık bir konteks penceresini sadece %2 KV cache ile nasıl sürdürebiliyor? CSA, HCA ve KV paylaşımı gibi yenilikçi teknikler, büyük dil modellerinin verimliliğinde bir de…

    链接 aihaberleri.org/…/deepseek-v4-2026-llm-mi…

相关实体

相关话题