DeepSeek V3.2-Exp — DeepSeek 的本地 AI 模型
作者： Jakub Rusinowski · 最后更新：
DeepSeek-V3.2-Exp, the experimental sparse-attention snapshot released in September 2025 ahead of the V3.2 line.
变体
DeepSeek V3.2-Exp 最小的变体在 Q4_K_M 下约需 414 GB 显存——量化权重加框架开销，不含 KV 缓存。
| 模型 | Q4 下显存 | 显存 | 上下文 | 运行 |
|---|---|---|---|---|
| DeepSeek V3.2-Exp 685B 685B (37B active) | ~414.4 GB | 128,000 | ollama run deepseek-v3-2-exp |
显存为 Q4_K_M 下的量化权重加开销，与 GPU 与显存检测器使用同一引擎计算。
如何在本地运行 DeepSeek V3.2-Exp
安装 Ollama，然后拉取标签。
ollama run deepseek-v3-2-exp在上方选择一个尺寸，查看它自己的显存、速度估算和安装命令。
许可证
Commercial use permitted. No usage restrictions beyond attribution.
适用于: DeepSeek V3.2-Exp 685BDeepSeek V3.2-Exp — 常见问题
How much VRAM does DeepSeek V3.2-Exp need?
DeepSeek V3.2-Exp needs about 414 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: DeepSeek V3.2-Exp 685B (414 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run DeepSeek V3.2-Exp on an RTX 4090 (24 GB)?
DeepSeek V3.2-Exp's smallest variant needs about 414 GB, which exceeds a single RTX 4090 (24 GB). Use multiple GPUs, a higher-VRAM card, or Apple Silicon with large unified memory.
What quantization should I use for DeepSeek V3.2-Exp?
Q4_K_M is the best balance of quality and VRAM for DeepSeek V3.2-Exp in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
How do I run DeepSeek V3.2-Exp with Ollama?
DeepSeek V3.2-Exp has no local Ollama tag — the published tag is cloud-hosted, so running it sends your prompts to a hosted GPU rather than your own machine.