Vultr Object Storage – Almacenamiento S3 para Cargas de IA
Almacenamiento de objetos escalable y compatible con S3, integrado con instancias GPU de Vultr. Almacena datasets de entrenamiento, checkpoints y artefactos de producción de forma asequible.
Object Storage Features
S3-Compatible API
Drop-in replacement for AWS S3. Use boto3, rclone, s3cmd, MinIO client, or the AWS CLI with a single endpoint change — no code rewrite needed.
Predictable Per-GB Pricing
Pay only for what you store. No per-request charges for standard GET/PUT operations. Ideal for large-scale ML dataset storage with high I/O frequency.
Global CDN Edge
Vultr's anycast CDN delivers static assets from 32+ global PoPs. Serve model inference responses, static ML artifacts, and API results at the edge.
Access Control & Encryption
Fine-grained S3 ACLs, bucket policies, and server-side AES-256 encryption. CORS configuration for web-facing API integrations.
GPU Instance Integration
Mount buckets via s3fs-fuse or stream data via boto3 directly from Vultr GPU instances. Co-located storage and compute minimize egress latency.
Unlimited Scalability
No object count limits. Store petabytes of training data, model weights, and checkpoints without pre-provisioning storage capacity.
Object Storage Pricing vs AWS S3 & GCS
| Feature | Vultr | AWS S3 | Google Cloud |
|---|---|---|---|
| Storage (per GB/mo) | ~$0.020 | ~$0.023 (S3 Standard) | ~$0.020 (Standard) |
| GET requests (10K) | Free | $0.004 | $0.004 |
| PUT requests (1K) | Free | $0.005 | $0.005 |
| Egress (per GB) | ~$0.01 (CDN) | $0.09 (Internet) | $0.08 (Internet) |
| S3 API Compatible | Yes | Native | Via XML API |
| GPU Co-location | Yes (same DC) | Partial | Partial |
AI & ML Storage Use Cases
ML Training Datasets
Store ImageNet, Common Crawl, or custom datasets. Stream multi-TB datasets directly to GPU training nodes using tf.data or PyTorch DataLoader with S3 connectors.
Model Weight Storage
Centralize GGUF, safetensors, ONNX, and checkpoint files. Version model weights with bucket versioning and restore previous checkpoints instantly.
Generative AI Assets
Store raw video/audio for fine-tuning multimodal models. Serve generated images and videos via Vultr CDN to end users without bandwidth spikes.
Database Backups
Automated PostgreSQL, MongoDB, and Redis backups to object storage. Lifecycle rules automatically archive old backups to cold storage tiers.
Quick Integration Examples
import boto3
s3 = boto3.client(
"s3",
endpoint_url="https://ewr1.vultrobjects.com",
aws_access_key_id="YOUR_ACCESS_KEY",
aws_secret_access_key="YOUR_SECRET_KEY",
)
# Upload dataset
s3.upload_file("dataset.tar.gz", "my-ml-bucket", "datasets/dataset.tar.gz")
# Stream to GPU instance
obj = s3.get_object(Bucket="my-ml-bucket", Key="models/llama3-70b.gguf")
data = obj["Body"].read()# Configure rclone rclone config create vultr s3 \ provider=Other \ endpoint=ewr1.vultrobjects.com \ access_key_id=YOUR_KEY \ secret_access_key=YOUR_SECRET # Sync datasets to GPU instance rclone sync vultr:my-ml-bucket/datasets ./datasets/ \ --transfers=32 --checkers=16 --progress
Related Technical Guides
Related Infrastructure Pages
Preguntas sobre Almacenamiento de Objetos
¿Es Vultr Object Storage compatible con AWS S3?
Sí. Vultr Object Storage usa la API compatible con S3, lo que significa que cualquier herramienta que funcione con AWS S3 — boto3, rclone, s3fs, cliente MinIO y AWS CLI — funciona nativamente con Vultr Object Storage con un simple cambio de endpoint.
¿Cuáles son las diferencias de precio vs AWS S3?
Vultr Object Storage es significativamente más económico que AWS S3. Vultr cobra por GB de almacenamiento sin tarifas por solicitud para operaciones estándar, lo que lo hace ideal para almacenamiento de grandes datasets ML con acceso frecuente de instancias GPU.
¿Puedo montar Vultr Object Storage directamente en una instancia GPU?
Sí. Puedes montar Vultr Object Storage usando s3fs-fuse o goofys, o acceder a él vía la API compatible con S3 desde Python (boto3) dentro de scripts de entrenamiento. Esto permite streaming de grandes datasets sin cuellos de botella de disco local.
¿Qué tipos de datos son más adecuados para Vultr Object Storage?
Datasets de entrenamiento ML, archivos de pesos de modelos (GGUF, safetensors), artefactos de inferencia, activos de video para IA generativa, copias de seguridad de bases de datos y activos de sitios web estáticos son todos ideales para almacenamiento de objetos.
Ready to Store AI Datasets on Vultr?
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