Running DeepSeek V4 Locally: Hardware, Steps & Pitfalls（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 ## Is local deployment suitable? If DeepSeek V4 ships open weights, local deployment gives data sovereignty and cost control. ## Hardware · ≤7B: single 16–24GB GPU / NPU · 13B–70B: dual 24GB GPU or 128GB unified memory · ≥120B: multi-GPU / high-bandwidth unified memory ## Steps 1. Pick model + quantization (GGUF/AWQ) 2. Pick stack (llama.cpp / vLLM / Ollama) 3. Benchmark 50–200 tasks 4. Connect via MCP / OpenAI-compatible API ## 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.