RunPod
Accelerate AI model development and deployment at scale
Target Audience
- AI developers
- ML engineers
- AI startup teams
- Research institutions
Hashtags
Overview
RunPod provides specialized cloud infrastructure for AI workloads, offering instant access to powerful GPUs and serverless scaling. Developers can deploy machine learning models using pre-configured environments or custom containers without managing hardware. It's designed to reduce setup time and costs while handling demanding tasks like training LLMs or processing millions of inference requests.
Key Features
50+ Prebuilt Templates
Instant deployment for PyTorch, TensorFlow, and custom environments
Global GPU Network
30+ regions with NVIDIA/AMD GPUs from $0.20/hr
Sub-250ms Cold Starts
Serverless scaling with near-instant GPU activation
Automatic Scaling
GPU workers scale from 0 to 100s based on demand
Network Storage
100TB+ NVMe storage with 100Gbps throughput
Use Cases
Train large language models
Deploy ML inference endpoints
Scale serverless AI workloads
Develop custom container environments
Pros & Cons
Pros
- Fast GPU deployment (seconds vs competitors' minutes)
- Wide range of modern GPUs including H100 and MI300X
- True pay-per-second billing model
- Enterprise-grade security with upcoming SOC2/GDPR compliance
Cons
- Limited managed services beyond infrastructure
Pricing Plans
MI300X Secure Cloud
hourlyFeatures
- AMD MI300X GPU
- 192GB VRAM
- 24 vCPUs
- 283GB RAM
H100 Community Cloud
hourlyFeatures
- NVIDIA H100 PCIe
- 80GB VRAM
- 24 vCPUs
- 188GB RAM
RTX 3090 Community Cloud
hourlyFeatures
- NVIDIA RTX 3090
- 24GB VRAM
- 4 vCPUs
- 24GB RAM
Pricing may have changed
For the most up-to-date pricing information, please visit the official website.
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