在Vultr GPU云上训练AI模型
访问NVIDIA A100和H100算力，运行PyTorch、JAX和TensorFlow训练任务。从单GPU实验扩展到分布式多GPU集群。
AI Training Methods on Cloud GPUs
Full Fine-Tuning
Update all model weights on your proprietary dataset. Requires significant VRAM — 70B models need 4–8× A100 80GB with ZeRO-3 optimizer offloading.
LoRA / QLoRA
Train low-rank adapter matrices instead of full weights. QLoRA cuts VRAM requirements by 4–5×, enabling 70B fine-tuning on a single A100 80GB.
RLHF / DPO
Align models with human preferences using Reinforcement Learning from Human Feedback or Direct Preference Optimization for instruction-following and safety.
Distributed Training
Scale across multiple A100/H100 GPUs with tensor parallelism, pipeline parallelism, and data parallelism. NVLink provides 600 GB/s GPU-to-GPU bandwidth.
GPU VRAM Requirements for Fine-Tuning
Estimates for common model sizes. Actual requirements vary by batch size, sequence length, and optimizer state.
| Method | VRAM Needed | Recommended Config |
|---|---|---|
| Full FT – 7B | ~60 GB | 1× A100 80GB |
| QLoRA – 7B | ~6 GB | Any GPU ≥ 8 GB |
| Full FT – 13B | ~120 GB | 2× A100 80GB |
| QLoRA – 13B | ~12 GB | 1× A100 80GB |
| Full FT – 70B | ~320 GB | 4× A100 80GB + ZeRO-3 |
| QLoRA – 70B | ~48 GB | 1× A100 80GB |
ML Training Frameworks on Vultr GPUs
PyTorch
Primary framework for custom training loops and research
TensorFlow / Keras
Production-grade training with TPU compatibility
JAX / Flax
Functional ML with XLA compilation for maximum throughput
HuggingFace Transformers
Largest model hub with ready-to-use training pipelines
DeepSpeed
Microsoft's distributed training library with ZeRO optimizers
LightningAI
Structured training loops with multi-GPU abstraction
Quick Start: Train a Model on Vultr GPU
- 1
Sign up for a new Vultr account via referral link (eligibility for promotional credits)
- 2
Select a GPU instance: A100 80GB for 13B–70B models, H100 for frontier workloads
- 3
Choose Ubuntu 22.04 with CUDA pre-installed, or deploy from a PyTorch Marketplace image
- 4
Install dependencies: pip install torch transformers peft bitsandbytes accelerate
- 5
Launch training: python train.py --model meta-llama/Llama-3-8B --method lora --dataset your_data.jsonl
Related Technical Guides
Related Infrastructure Pages
AI训练常见问题
哪些框架可以在 Vultr GPU 服务器上用于训练？
Vultr GPU 实例支持所有主要的 ML 框架，包括 PyTorch、TensorFlow、JAX 和 MXNet。
在 Vultr 上进行 GPU 训练需要多少费用？
云 GPU 训练消除了前期硬件成本。Vultr 的按小时定价让您可以运行训练突发，并仅为使用的计算资源付费。
我可以在 Vultr GPU 上微调 70B LLM 吗？
可以。使用 QLoRA 或 LoRA 微调，70B 模型可以在 2-4 个 A100 80GB 实例上进行微调。
Vultr 支持分布式训练吗？
是的。Vultr 支持带有 NVLink 的多 GPU 实例，使用 PyTorch DDP 或 DeepSpeed 进行张量并行。
Start Training on Vultr GPUs
New accounts signed up via referral link may be eligible for promotional credits. Credits subject to Vultr's official program terms.