Mistral Large 4 — Mistral AI 的本地 AI 模型
作者： Jakub Rusinowski · 最后更新：
Mistral 的开放权重旗舰模型，在仅提供 API 的三周预览之后，于 2026 年 10 月 29 日公开权重：一个混合专家（MoE）模型，总参数约 1 万亿，每个 token 激活 52B，支持 100 万 token 上下文和图像输入。所有专家都必须常驻内存，因此自行部署属于数据中心级别；唯一的单机方案是在 512 GB 统一内存上运行 2 位 GGUF。
变体
Mistral Large 4 最小的变体在 Q4_K_M 下约需 636 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 |
|---|---|---|---|---|
| Mistral Large 4 1T-A52B → 1T (52B active) | ~635.6 GB | 1,048,576 | 云端托管标签 |
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 Mistral Large 4
安装 Ollama，然后拉取标签。
Ollama lists Mistral Large 4 only as the cloud tag mistral-large-4:cloud, which runs on Ollama's servers, not your machine. Locally, use llama.cpp (mainline since 29 October 2026) with the ggml-org GGUFs, or vLLM / SGLang on a multi-GPU server.
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
Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.
适用于: Mistral Large 4 1T-A52BMistral Large 4 — 常见问题
How much VRAM does Mistral Large 4 need?
Mistral Large 4 needs about 636 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Mistral Large 4 1T-A52B (636 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run Mistral Large 4 on an RTX 4090 (24 GB)?
Mistral Large 4's smallest variant needs about 636 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 Mistral Large 4?
Q4_K_M is the best balance of quality and VRAM for Mistral Large 4 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 Mistral Large 4 with Ollama?
Mistral Large 4 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.