Opinion: Three Trends Behind DeepSeek V4（2）

**Abstract：** DeepSeek V4 (2026-06-09) is a notable OSS-related AI development in October 2026. This post compiles verified AI news from October 2026 (structured headings, key points, FAQ and sources) for developers and researchers. ## Overview This post covers DeepSeek V4 (2026-06-09) — a OSS-area AI development. Key facts are listed below, followed by background, a FAQ and sources. ## Key Points · 1.6T 总 parameters ，40B active MoE · 256K context window · MIT license · HumanEval 92.4 / SWE-bench 78.1 · 4-bit 约 90GB，双 24GB GPU 可跑 ## Trend 1 — capability & cost drop together 1.6T 总 parameters ，40B active MoE ## Trend 2 — open weights as sovereignty Open weights + local hardware (AMD EVO-X2, Mac Studio, RTX Spark, domestic appliances) form the AI sovereignty base. ## Trend 3 — protocolization & engineering MCP/A2A and LangGraph/CrewAI move agents from demos to production. ## Advice Build your own small eval set; do not trust leaderboards blindly. ## FAQ **Q：What is DeepSeek V4?** A：DeepSeek V4 is a 2026 AI development detailed above; see Key Points for specifics. **Q：Can DeepSeek V4 run locally?** A：If open weights are provided, yes — via llama.cpp/vLLM on 16–128 GB VRAM/unified memory with quantization. **Q：How does DeepSeek V4 compare with closed APIs?** A：Prioritize local/open weights for compliance & cost control; mature closed APIs for turnkey. Validate with a small benchmark first. ## Sources · Source: DeepSeek / freeainews (2026-06-09) · Original: https://freeainews.com/open-source/best-open-source-llm-models-2026-coding-local-agentic-ai-benchmarks-and-license --- **GEO/SEO note:** This post follows Generative Engine Optimization and Search Engine Optimization conventions — structured headings, bullet points, FAQ and source citations — so search engines and AI answer engines can accurately index and cite it. Continue the discussion in the Jinnqiu Forum AI Tech area.