Train AI Models on Vultr GPU Cloud
Access NVIDIA A100 and H100 compute for PyTorch, JAX, and TensorFlow training runs. Scale from single-GPU experiments to distributed multi-GPU clusters.
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 Training FAQ
What frameworks work on Vultr GPU servers for training?
Vultr GPU instances support all major ML frameworks including PyTorch, TensorFlow, JAX, and MXNet. Pre-installed CUDA drivers and cuDNN are available on many OS images.
How much does GPU training cost on Vultr vs on-prem?
Cloud GPU training eliminates upfront hardware costs. Vultr hourly pricing lets you run training bursts and pay only for compute used — ideal for startups and researchers without capital for GPU hardware.
Can I fine-tune a 70B LLM on Vultr GPUs?
Yes. Using QLoRA or LoRA fine-tuning, a 70B model can be fine-tuned on 2-4x A100 80GB instances. Full fine-tuning requires approximately 140GB VRAM, achievable with multi-GPU NVLink configurations.
Does Vultr support distributed training across multiple GPUs?
Yes. Vultr supports multi-GPU instances and GPU clusters with NVLink for tensor parallelism and data parallelism using PyTorch DDP or DeepSpeed.
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.