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Compare GPU and LLM inference API pricing between EcoHash and IO.NET. Find the best rates for AI training, inference, and ML workloads.
Provider 1
Provider 2
Average Price Difference: $0.09/hour between comparable GPUs
| GPU Model ↑ | EcoHash Price | IO.NET Price | Price Diff ↕ | Sources |
|---|---|---|---|---|
A100 PCIE 40GB VRAM • IO.NET | Not Available | — | ||
A100 PCIE 40GB VRAM • | ||||
A100 SXM 80GB VRAM • IO.NET | Not Available | 2x GPU | — | |
A100 SXM 80GB VRAM • | ||||
A30 24GB VRAM • IO.NET | Not Available | 4x GPU | — | |
A30 24GB VRAM • | ||||
A40 48GB VRAM • IO.NET | Not Available | — | ||
A40 48GB VRAM • | ||||
Gaudi 2 96GB VRAM • IO.NET | Not Available | 8x GPU | — | |
Gaudi 2 96GB VRAM • | ||||
H100 SXM 80GB VRAM • IO.NET | Not Available | 4x GPU | — | |
H100 SXM 80GB VRAM • | ||||
H200 141GB VRAM • IO.NET | Not Available | — | ||
H200 141GB VRAM • | ||||
L4 24GB VRAM • IO.NET | Not Available | 8x GPU | — | |
L4 24GB VRAM • | ||||
L40 40GB VRAM • IO.NET | Not Available | — | ||
L40 40GB VRAM • | ||||
L40S 48GB VRAM • IO.NET | Not Available | 4x GPU | — | |
L40S 48GB VRAM • | ||||
RTX 4000 Ada 20GB VRAM • IO.NET | Not Available | — | ||
RTX 4000 Ada 20GB VRAM • | ||||
RTX 6000 Ada 48GB VRAM • IO.NET | Not Available | — | ||
RTX 6000 Ada 48GB VRAM • | ||||
RTX A6000 48GB VRAM • IO.NET | Not Available | 2x GPU | — | |
RTX A6000 48GB VRAM • | ||||
RTX PRO 6000 96GB VRAM • EcoHashIO.NET | 8x GPU | ↓$0.09(4.6%) | ||
RTX PRO 6000 96GB VRAM • $1.89/hour Updated: 9/17/2026 ★Best Price $1.98/hour 8x GPU configuration Updated: 9/20/2026 Price Difference:↓$0.09(4.6%) | ||||
Tesla T4 16GB VRAM • IO.NET | Not Available | — | ||
Tesla T4 16GB VRAM • | ||||
A100 PCIE 40GB VRAM • IO.NET | Not Available | — | ||
A100 PCIE 40GB VRAM • | ||||
A100 SXM 80GB VRAM • IO.NET | Not Available | 2x GPU | — | |
A100 SXM 80GB VRAM • | ||||
A30 24GB VRAM • IO.NET | Not Available | 4x GPU | — | |
A30 24GB VRAM • | ||||
A40 48GB VRAM • IO.NET | Not Available | — | ||
A40 48GB VRAM • | ||||
Gaudi 2 96GB VRAM • IO.NET | Not Available | 8x GPU | — | |
Gaudi 2 96GB VRAM • | ||||
H100 SXM 80GB VRAM • IO.NET | Not Available | 4x GPU | — | |
H100 SXM 80GB VRAM • | ||||
H200 141GB VRAM • IO.NET | Not Available | — | ||
H200 141GB VRAM • | ||||
L4 24GB VRAM • IO.NET | Not Available | 8x GPU | — | |
L4 24GB VRAM • | ||||
L40 40GB VRAM • IO.NET | Not Available | — | ||
L40 40GB VRAM • | ||||
L40S 48GB VRAM • IO.NET | Not Available | 4x GPU | — | |
L40S 48GB VRAM • | ||||
RTX 4000 Ada 20GB VRAM • IO.NET | Not Available | — | ||
RTX 4000 Ada 20GB VRAM • | ||||
RTX 6000 Ada 48GB VRAM • IO.NET | Not Available | — | ||
RTX 6000 Ada 48GB VRAM • | ||||
RTX A6000 48GB VRAM • IO.NET | Not Available | 2x GPU | — | |
RTX A6000 48GB VRAM • | ||||
RTX PRO 6000 96GB VRAM • EcoHashIO.NET | 8x GPU | ↓$0.09(4.6%) | ||
RTX PRO 6000 96GB VRAM • $1.89/hour Updated: 9/17/2026 ★Best Price $1.98/hour 8x GPU configuration Updated: 9/20/2026 Price Difference:↓$0.09(4.6%) | ||||
Tesla T4 16GB VRAM • IO.NET | Not Available | — | ||
Tesla T4 16GB VRAM • | ||||
| Model ↑ | EcoHash | IO.NET | Input Diff ↕ |
|---|---|---|---|
DeepSeek | Not available | $0.568 in $2.28 out | — |
DeepSeek | Not available | $1.42 in $2.38 out | — |
DeepSeek | Not available | $0.345 in $0.582 out | — |
DeepSeek | $0.910 in $2.72 out | $1.45 in $2.90 out | $0.540 |
DeepSeek | Not available | $0.300 in $1.20 out | — |
Google | Not available | $0.100 in $0.374 out | — |
Zhipu | Not available | $0.064 in $0.400 out | — |
Zhipu | Not available | $0.157 in $0.937 out | — |
Zhipu | Not available | $0.520 in $2.04 out | — |
Zhipu | Not available | $0.880 in $2.37 out | — |
Zhipu | Not available | $0.860 in $2.75 out | — |
Zhipu | Not available | $1.27 in $3.99 out | — |
Zhipu | $1.00 in $3.00 out | $2.13 in $6.88 out | $1.13 |
Z AI | $1.25 in $4.30 out | $1.38 in $4.40 out | $0.130 |
Z AI | Not available | $0.210 in $0.700 out | — |
Explore how these providers compare to other popular GPU cloud services
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Compare EcoHash with another leading provider
Compare EcoHash with another leading provider
Compare EcoHash with another leading provider
Compare EcoHash with another leading provider
Compare EcoHash with another leading provider
Offers configurations with 1, 2, 4, or 8 RTX Pro 6000 GPUs.
Supports automatic selection of LoRA/QLoRA fine-tuning over 1-4 GPUs.
Provides an API compatible with OpenAI for various AI models.
Users get full root access with shared filesystems on RTX Pro environments.
Inference endpoints can be deployed across multiple regions with failover.
Access to 300,000+ verified GPUs from 139 countries with 6,000+ cluster-ready GPUs
Deploy clusters in under 90 seconds with auto-scaling capabilities
Choose from containers, Ray clusters, or bare metal based on workload needs
Uses the same distributed computing framework that OpenAI used to train GPT-3
AI models, smart agents, and API integration for workflow automation
Kernel-level VPN with secure mesh protocols for data protection
On-demand GPU clusters for AI/ML workloads with multiple deployment options
AI models, smart agents, and API integration platform
Decentralized pool of GPU providers with unified APIs and competitive pricing.
Most cost-effective option for distributed ML workloads using Ray framework
Standard containerized deployments with Docker support
Premium pricing for direct hardware access and maximum performance
Dynamic pricing based on actual resource usage with automatic scaling
Create an account on the EcoHash platform.
Select the desired GPU instances for your project.
Consult the EcoHash documentation to understand available features.
Launch your GPU environment for training or inference.
Start using the API for model inference or training tasks.
Create an account on the IO.NET platform with no complex KYC requirements
Purchase $IO tokens for compute payments or add other supported payment methods
Select from containers, Ray clusters, or bare metal based on your workload
Specify GPU requirements, region preferences, and scaling options
Launch your cluster in under 90 seconds and start your AI/ML workloads
Available across multiple global regions, including the Americas.
Offers support via documentation and community channels.
Global distributed network across 139 countries with intelligent geographic clustering and latency optimization
Documentation portal, Discord community (500,000+ members), Telegram support, and direct engineering support for GPU and driver questions