io.net is the world's largest decentralized GPU cloud platform, aggregating over 200,000 GPUs from independent providers worldwide into a single on-demand compute network. Think of it as the "Airbnb of GPU infrastructure"—connecting teams that need high-performance compute for AI training and inference with GPU owners who have idle capacity.

For AI engineers and ML teams, io.net solves two critical problems that plague traditional cloud providers: prohibitive costs and scarce availability. While AWS charges $98/hour for 8x H100 GPUs (with 6-month waitlists), io.net offers the same hardware for $28-32/hour with instant deployment. That's 70% cost savings without sacrificing performance or reliability.

This comprehensive guide explains how io.net works, how it compares to traditional cloud providers, what it costs, and how to get started training your first AI model on decentralized GPU infrastructure.

How io.net Works - Decentralized GPU Infrastructure Explained

Unlike AWS, Google Cloud, or Azure—which own and operate centralized data centers—io.net operates a decentralized network of GPU providers. Here's how the platform creates a reliable cloud service from distributed hardware.

The GPU Provider Network

io.net's infrastructure consists of thousands of independent GPU providers contributing compute capacity:

Independent data centers: Small-to-medium compute providers with 10-100 GPUs who want to monetize idle capacity

Enterprise operators: Companies with private GPU clusters that rent excess capacity during off-peak hours

Blockchain validators: Crypto mining operations diversifying into AI compute

Research institutions: Universities and labs offering spare GPU time when not running experiments

This distributed supply model creates fundamentally different economics than hyperscalers. No massive capital expenditure on data center construction. No multi-billion dollar GPU procurement contracts. Just a marketplace connecting supply (GPU providers) with demand (ML engineers).

The result: io.net can offer H100 GPUs at $4/hour while AWS charges $12/hour for the same hardware.

Verification and Quality Control

A natural concern with decentralized infrastructure: "How do I know the GPUs are real and performant?"

io.net implements multi-layer verification:

Hardware attestation: Every GPU proves its identity through cryptographic signatures from the GPU firmware. You can't fake an H100—the hardware must cryptographically attest to its model, memory capacity, and CUDA capabilities.

Benchmark validation: Before accepting GPU capacity into the network, io.net runs standardized benchmarks (matrix multiplication, memory bandwidth, multi-GPU communication) to verify performance meets specifications.

Reputation scoring: Providers build reputation over time. GPUs with 99%+ uptime and verified performance get higher visibility. Providers with failures or performance issues get demoted.

Real-time monitoring: io.net continuously monitors GPU health (temperature, memory errors, clock speeds). If a GPU degrades, it's automatically removed from the available pool.

The outcome: io.net maintains 99.9% uptime SLA comparable to hyperscaler standards, despite the distributed architecture.

Instant Provisioning - No Waitlists

When you request an 8-GPU H100 cluster on io.net, here's what happens:

  1. Matchmaking (10 seconds): io.net's scheduler finds available H100 GPUs that meet your requirements (GPU type, quantity, region preference, network topology)

  2. Provisioning (60 seconds): Selected GPUs are reserved, networking is configured, and your container environment is prepared

  3. Deployment (30 seconds): Your training container is deployed across the GPU cluster and health checks complete

Total time: <2 minutes from request to SSH-accessible cluster.

Compare this to AWS, where requesting P5 instances (8x H100) typically requires:

  • Submitting a limit increase request (1-2 days)
  • Waiting for capacity allocation (weeks to months)
  • Reserving instances with long-term commitments

io.net's distributed supply model means there's always available capacity somewhere in the network. When demand spikes, the network draws from global GPU inventory instead of hitting capacity limits in specific AWS regions.

Container-Based Deployment

io.net uses a container-first architecture. You don't get SSH access to bare metal servers—you deploy Docker containers that run on the GPU infrastructure.

Why containers matter:

  • Portability: Your training code works identically on io.net, AWS, GCP, or your local workstation
  • Security: Container isolation protects your code and data from other users sharing the same physical hardware
  • Reproducibility: Container images capture your exact dependency environment
  • No vendor lock-in: Move workloads between io.net and other providers without code changes

Standard workflow:

# Build your training container
docker build -t my-training-job .

# Deploy to io.net (8x H100 cluster)
ionet deploy --gpus 8 --gpu-type h100-sxm my-training-job

Your container runs on io.net GPUs with NVIDIA drivers, CUDA toolkit, and networking configured automatically.

io.net vs AWS/GCP/Azure - Key Differences

How does io.net compare to traditional cloud providers across the dimensions that matter for ML workloads?

Pricing Comparison (70% Cheaper)

ConfigurationAWS (on-demand)GCP (on-demand)Azure (on-demand)io.netio.net Savings
8x H100 SXM$98.32/hr$89.60/hr$91.44/hr$28-32/hr68-71%
8x A100 80GB$40.96/hr$36.48/hr$38.24/hr$18-22/hr46-56%
Single H100 PCIe$12.29/hr$11.20/hr$11.43/hr$3.50-4.20/hr66-71%
Single A100 80GB$5.12/hr$4.56/hr$4.78/hr$2.50-3.00/hr42-51%

Prices shown are compute-only. AWS/GCP/Azure add egress fees ($0.09-0.12/GB), storage markups, load balancer charges, and other hidden costs. io.net pricing includes all infrastructure—no surprise fees.

Example: Training LLaMA 2 70B (64 GPUs × 30 days)

  • AWS cost: ~$599,000
  • io.net cost: ~$173,000
  • Savings: $426,000 (71%)

Availability (Instant vs Waitlists)

AWS/GCP/Azure availability (H100 instances as of April 2026):

  • AWS P5: 3-6 month waitlist, limited regions (us-east-1, us-west-2)
  • GCP A3: 4-8 week quota approval, sparse regional availability
  • Azure ND H100 v5: Extremely limited, enterprise agreements often required

io.net availability:

  • H100 SXM: Instant (<2 min), no waitlist, global coverage
  • A100: Instant (<2 min), no waitlist
  • All GPU types: True on-demand without reservation requirements

This availability difference isn't just convenience—it's competitive advantage. When your competitor has to wait 4 months for AWS H100 capacity, you're already training and iterating on io.net.

Flexibility (No Commitments)

Hyperscaler model: To get reasonable pricing, you commit to 1-3 year reserved instances. This locks you into:

  • Specific instance types (can't switch from A100 to H100 without penalty)
  • Minimum capacity (pay for GPUs even when not training)
  • Vendor ecosystem (switching costs compound over time)

io.net model: True pay-per-hour with zero commitments.

  • Scale to zero when not training (only pay for active GPU time)
  • Switch between GPU types instantly (H100 for training, A100 for experiments)
  • Move workloads to other providers anytime (container portability)

For startups and research teams with spiky workloads, this flexibility is worth 10-20% premium even if pricing were equal. At 70% savings, it's a no-brainer.

Control (Containers vs Managed Services)

AWS/GCP/Azure approach: Managed services (SageMaker, Vertex AI, Azure ML) abstract away infrastructure but impose proprietary APIs.

io.net approach: You manage your own training orchestration using standard tools (Docker, Kubernetes, Ray). This requires more infrastructure knowledge but delivers full control and zero lock-in.

When managed services make sense: If you want zero infrastructure management and are willing to pay 20-40% premium plus accept vendor lock-in, SageMaker/Vertex AI deliver value.

When io.net makes sense: If you already use containers, value cost savings over convenience, or want to avoid proprietary cloud APIs, io.net's open infrastructure is superior.

io.net Pricing - Transparent GPU Rental Costs

io.net pricing is radically simpler than hyperscaler models. No reserved instances, no savings plans, no complex calculators—just transparent per-hour rates.

Current Pricing (April 2026)

H100 GPUs:

  • H100 SXM 80GB: $3.50-4.00/hour per GPU ($28-32/hr for 8-GPU cluster)
  • H100 PCIe 80GB: $3.50-4.20/hour per GPU

A100 GPUs:

  • A100 SXM 80GB: $2.50-2.75/hour per GPU ($20-22/hr for 8-GPU cluster)
  • A100 PCIe 80GB: $2.50-3.00/hour per GPU
  • A100 SXM 40GB: $1.80-2.20/hour per GPU

RTX 4090 (for inference/fine-tuning):

  • RTX 4090 24GB: $0.90-1.20/hour per GPU

What's included:

  • GPU compute
  • Network bandwidth (no egress fees)
  • Storage for containers and checkpoints
  • Monitoring and dashboards
  • Support (community Discord + paid enterprise support)

What you pay for separately:

  • External data transfer to/from S3/GCS (standard provider rates)
  • Third-party software licenses (bring your own)

No Hidden Fees

Hyperscalers bury costs in egress fees, storage markups, and service charges. io.net pricing is all-inclusive:

AWS surprise fees:

  • Data egress: $0.09/GB after first 100GB
  • EBS storage: $0.08-0.15/GB/month
  • Load balancers: $0.0225/hour + $0.008/GB
  • VPC networking: Various charges
  • CloudWatch metrics: $0.30 per metric per month

io.net fees:

  • None. The hourly GPU rate is the total cost.

Cost Calculator Examples

Example 1: Fine-tuning Stable Diffusion XL

  • Workload: 100K steps, 8x A100 80GB, 7 days
  • AWS cost: $40.96/hr × 168 hrs = $6,881
  • io.net cost: $20/hr × 168 hrs = $3,360
  • Savings: $3,521 (51%)

Example 2: Training LLaMA 2 13B

  • Workload: 14 days, 16x H100 SXM
  • AWS cost: $98.32/hr × 2 clusters × 336 hrs = $66,071
  • io.net cost: $30/hr × 2 clusters × 336 hrs = $20,160
  • Savings: $45,911 (69%)

Example 3: Batch inference (GPT-3 175B)

  • Workload: 1M tokens/day, single H100, 30 days
  • AWS cost: $12.29/hr × 24 hrs × 30 days = $8,849
  • io.net cost: $4/hr × 24 hrs × 30 days = $2,880
  • Savings: $5,969 (67%)

Use Cases - What io.net is Best For

io.net excels for specific AI workload categories. Here's where it shines and where alternatives might be better.

Ideal Use Cases

Large Language Model Training
When training LLMs from scratch or fine-tuning large models, compute cost dominates your budget. io.net's 70% savings on H100/A100 clusters directly translates to lower project costs. Teams training LLaMA, GPT-style models, or domain-specific LLMs save $100K-500K per training run.

Research and Experimentation
Academic labs and research teams with limited budgets can access cutting-edge GPUs that would be unaffordable on AWS. Run 3 experiments on io.net for the cost of 1 on AWS. That velocity compounds into more publications and faster discoveries.

Startup ML Workloads
For startups, every dollar of runway matters. Spending $50K/month on AWS GPUs vs $15K/month on io.net is the difference between 12 months and 18 months of runway—potentially make-or-break for pre-revenue AI companies.

Batch Inference
Running batch inference jobs (processing datasets overnight, generating embeddings, etc.) benefits from io.net's no-commitment model. Spin up 64 GPUs for 4 hours, process your batch, shut down. No reserved instances wasted.

Burst Training
Many teams have baseline compute needs (a few GPUs for development) with periodic spikes (large training runs). Use AWS/GCP for steady-state, io.net for bursts. Best of both worlds.

When to Consider Alternatives

Production real-time inference with strict SLAs
If you're serving user-facing traffic with <100ms latency requirements and need enterprise SLAs, managed inference endpoints (SageMaker, Vertex AI) might justify their premium.

Deep AWS ecosystem integration
If your entire stack runs on AWS (data in S3, pipelines in Step Functions, monitoring in CloudWatch), SageMaker's integration might outweigh io.net's cost savings—at least until you're ready to reduce AWS dependency.

Compliance requires specific cloud providers
Some regulated industries require FedRAMP, HIPAA, or ISO certifications from specific cloud vendors. io.net is building compliance certifications but hyperscalers currently have more comprehensive programs.

How to Get Started on io.net

Getting your first training job running on io.net takes less than an hour. Here's the step-by-step process.

Step 1: Sign Up and Add Credits

  1. Go to cloud.io.net and create an account
  2. Verify email address
  3. Add credits:
    • Credit card: Visa, Mastercard, Amex accepted
    • Crypto: USDC, USDT, ETH, BTC, IO token
    • Free trial: Claim $100 in credits (no credit card required)

No contracts to sign. No sales calls required. Just create account and start deploying.

Step 2: Deploy Your First Cluster

Via Web Dashboard:

  1. Click "Deploy Cluster"
  2. Select GPU type (H100 SXM, A100 80GB, etc.)
  3. Choose quantity (1-64+ GPUs)
  4. Select region preference (optional - auto-select is fine)
  5. Click "Launch"

Via CLI (for automation):

# Install io.net CLI
pip install ionet-cli

# Authenticate
ionet login

# Deploy 8x H100 cluster
ionet cluster create --gpu h100-sxm --count 8 --name my-training-cluster

Cluster provisions in <2 minutes. You'll receive SSH connection details and Kubernetes config.

Step 3: Run Your Training Job

Option A: SSH Access (simple testing)

# SSH into cluster
ssh -i ~/.ssh/ionet_key ubuntu@<cluster-ip>

# GPUs available via standard NVIDIA drivers
nvidia-smi

# Run training script
python train.py

Option B: Container Deployment (recommended)

# Build training container
docker build -t my-llm-training .

# Deploy to io.net cluster
ionet deploy --cluster my-training-cluster --image my-llm-training

Option C: Kubernetes (for complex workflows)

# Get kubeconfig
ionet cluster kubeconfig my-training-cluster > ~/.kube/ionet-config

# Deploy via kubectl
kubectl apply -f training-job.yaml

Your training code runs identically to how it would on AWS, GCP, or local workstations. io.net provides GPU infrastructure—you control everything else.

Step 4: Monitor and Scale

Monitoring:

  • io.net dashboard shows real-time GPU utilization, memory usage, and cost accumulation
  • Export metrics to Prometheus/Grafana for custom dashboards
  • Set budget alerts to prevent runaway costs

Scaling:

# Scale cluster to 16 GPUs
ionet cluster scale my-training-cluster --count 16

# Scale to zero (stop charges)
ionet cluster scale my-training-cluster --count 0

Pay only for active GPU time. When you're analyzing results or preparing the next experiment, scale to zero.

Frequently Asked Questions

Is io.net reliable for production workloads?

Yes. io.net maintains 99.9% uptime SLA through distributed redundancy. Unlike centralized clouds where a data center outage impacts all customers, io.net's decentralized architecture routes around failures automatically.

That said, for user-facing inference with strict latency SLAs, managed hyperscaler services still have an edge. io.net is production-ready for training and batch workloads; consider it carefully for real-time serving.

How does io.net ensure GPU quality?

Three-layer verification:

  1. Cryptographic attestation: GPUs prove their hardware identity
  2. Benchmark validation: Performance testing before acceptance
  3. Continuous monitoring: Real-time health checks

If a GPU underperforms or fails, it's automatically replaced from the pool. You don't deal with hardware issues—io.net handles it.

Can I use my existing ML code on io.net?

Yes, with no modifications. If your code runs in a Docker container (or can be containerized), it works on io.net. Standard frameworks (PyTorch, TensorFlow, JAX, HuggingFace) run identically.

The only adjustment: replace provider-specific APIs (SageMaker SDK, Vertex AI client libs) with standard alternatives (S3 via boto3, GCS via gsutil, etc.).

What payment methods does io.net accept?

  • Credit/debit cards (Visa, Mastercard, Amex)
  • Cryptocurrency (USDC, USDT, ETH, BTC, IO token)
  • Wire transfer (for enterprise accounts >$10K)
  • Net-30 invoicing (for qualified enterprise customers)

Most users start with credit card for fast onboarding, then switch to crypto or invoicing for larger usage.

How does io.net pricing compare to AWS spot instances?

AWS spot instances offer 60-90% discounts but can be terminated with 30 seconds notice. For multi-day training, spot interruptions require checkpoint-restart logic and often waste compute when preempted mid-batch.

io.net's standard pricing ($4/hr H100) is cheaper than AWS spot ($45-60/hr for P5 spot) and provides stable, non-preemptible compute. You get better economics without the operational complexity of spot instance management.

Conclusion

io.net represents a fundamental shift in how AI teams access GPU infrastructure. By aggregating underutilized GPUs from thousands of independent providers into a decentralized cloud, io.net delivers the same high-performance hardware as AWS/GCP—but at 70% lower cost with instant availability and zero lock-in.

For ML engineers, the value proposition is clear:

  • Save 70% on GPU compute costs (H100 for $4/hr vs AWS $12/hr)
  • Deploy instantly without 6-month waitlists
  • Maintain flexibility with no reservations or commitments
  • Avoid lock-in through container-based portability

The question isn't whether decentralized GPU clouds work—io.net already powers training for thousands of AI teams. The question is how long you'll keep overpaying hyperscalers when there's a better alternative.

Ready to experience 70% GPU cost savings?

→ View pricing calculator for your workload
→ Read setup guide for deployment walkthrough

About io.net: io.net operates the world's largest decentralized GPU cloud. We help ML teams reduce cloud costs by 70% while eliminating capacity constraints. Learn more at io.net.