想跑大模型、Stable Diffusion、做推理？先搞清：普通 VPS 跑不动。
普通 VPS vs GPU 云
普通 VPS 是 CPU-only，跑大模型训练 / 推理太慢甚至跑不了。需要带 NVIDIA GPU 的云服务器。
关键看什么
- 显存（VRAM）：最关键。7B 模型约需 16G、13B 约 24G，更大要多卡；推理比训练省显存，量化后更省。
- 卡型：消费级（RTX 4090 / 3090）便宜、跑推理够用；专业卡（A100 / H100）贵、适合训练 / 生产。
- 计费方式：GPU 常按小时计费，跑完即停最省；长期跑再看包月。
推荐
- 个人推理 / 学习 → 4090 / 3090 按小时租。
- 训练 / 生产 → A100 系，按需。
提醒
GPU 云贵，先算清显存需求别买大；推理优先用量化模型省显存省钱。普通建站 / 科学上网用不到 GPU，别花冤枉钱。
按上面的需求挑机？去 库存监控 按线路 / 地区筛选在售机型。
Want to run large language models, Stable Diffusion, or do inference workloads? First, get one thing straight: ordinary VPS instances won’t cut it.
Ordinary VPS vs. GPU Cloud
A standard VPS is CPU-only. Training or running inference on large models is either too slow or outright impossible without a cloud server with an NVIDIA GPU.
What to Look For
- VRAM (Video Memory): The most critical factor. A 7B model needs roughly 16 GB, a 13B model around 24 GB, and larger models require multiple GPUs. Inference uses less VRAM than training, and quantization reduces it further.
- GPU model: Consumer GPUs (RTX 4090 / 3090) are cheaper and fine for inference; professional cards (A100 / H100) are more expensive but suited for training and production workloads.
- Billing model: GPU cloud is commonly billed hourly — stop the instance when done to minimize cost. Only consider monthly plans for sustained workloads.
Recommendations
- Personal inference / learning → rent a 4090 / 3090 by the hour.
- Training / production → A100-class GPUs, on demand.
Heads Up
GPU cloud is expensive — calculate your VRAM requirements before purchasing so you don’t overpay for unused capacity. Prioritize quantized models for inference to save VRAM and money. For ordinary web hosting or proxy use cases, you don’t need a GPU — don’t overspend.
Ready to find a machine that fits? Go to the stock monitor to filter available plans by route and region.
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