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DeepSeek V4 launches with 1.6T MoE, 1M context, and lower costs

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 4 sources

DeepSeek V4, an open-weight model family, has been released with a 1.6-trillion-parameter Mixture-of-Experts architecture that activates only 49 billion parameters per token. This new model boasts a 1-million-token context window and significantly reduced inference costs, achieving up to 73% lower costs than its predecessor due to innovations like Hybrid Attention. The V4 family, available on Hugging Face, offers comparable quality to leading models like GPT-5.4 and Claude Opus 4.6 at a fraction of the price, with optimized hardware performance for NVIDIA Blackwell. AI

Summary written by gemini-2.5-flash-lite from 4 sources. How we write summaries →

IMPACT Sets a new standard for efficiency in large MoE models, making advanced AI capabilities more accessible and affordable for developers.

RANK_REASON New model release from DeepSeek, a significant AI lab, with detailed technical specifications and benchmark comparisons.

Read on dev.to — LLM tag →

DeepSeek V4 launches with 1.6T MoE, 1M context, and lower costs

COVERAGE [4]

  1. Mastodon — fosstodon.org TIER_1 · [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…

    LINKS servermo.com/…/self-host-deepseek-v4-bare…
  2. dev.to — LLM tag TIER_1 · 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 · 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…

    LINKS 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…

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

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