You need H100 GPUs for your LLM training job. AWS has them through EC2 P5 instances—but can you actually access them, and what will it really cost?
The reality of NVIDIA H100 on AWS in 2026 differs sharply from marketing materials. While AWS P5 instances deliver powerful H100 Tensor Core GPUs, accessing them means navigating 8-week Capacity Block reservations, opaque pricing structures, and monthly costs exceeding $70,000 for an 8-GPU cluster. For AI teams evaluating cloud GPU options, understanding both AWS's offering and alternatives is critical.
This comprehensive guide examines AWS H100 specifications, real pricing (including hidden costs), availability constraints, and performance benchmarks. We'll also compare AWS P5 instances to alternative providers—specifically io.net's decentralized GPU cloud, which offers instant H100 deployment at 70-80% lower cost with zero commitment.
By the end, you'll understand exactly what AWS H100 offers, know the real total cost of ownership, discover faster and cheaper alternatives with instant access, and have actionable next steps to deploy H100 GPUs today.
What Are NVIDIA H100 GPUs?
The NVIDIA H100 is the company's flagship AI accelerator, launched in March 2022 as the successor to the A100. Built on the Hopper architecture, H100 GPUs deliver breakthrough performance for large language model training, generative AI inference, and high-performance computing workloads.
H100 Technical Specifications
The H100 represents a generational leap over previous GPU architectures:
GPU Memory: 80GB HBM3 memory with 3.35 TB/s bandwidth (vs. 40-80GB HBM2e on A100)
Compute Performance:
- FP64 (scientific computing): 34 teraFLOPS
- FP32 (single precision): 67 teraFLOPS
- TF32 Tensor Core: 989 teraFLOPS
- FP16 Tensor Core: 1,979 teraFLOPS
- FP8 Tensor Core: 3,958 teraFLOPS (with Transformer Engine)
Interconnect:
- NVLink 4.0: 900 GB/s bidirectional per GPU
- PCIe Gen5: 128 GB/s
Power:
- SXM form factor: 700W TDP
- PCIe form factor: 350W TDP
The 80GB HBM3 memory is particularly significant for large language models. Training GPT-scale models with 175B+ parameters requires massive GPU memory—H100's 80GB enables fitting larger models per GPU, reducing the number of GPUs needed and improving training efficiency.
H100 vs. A100: What's the Performance Gap?
Real-world benchmarks show substantial H100 advantages over A100:
LLM Training: 3-4x faster on transformer models like GPT-3, LLaMA, and Claude. The Transformer Engine, which automatically converts between FP8 and FP16 precision, accelerates attention mechanisms while maintaining accuracy.
Inference Throughput: 2-3x improvement for generative AI inference. Stable Diffusion XL image generation sees 2.5x speedup, enabling real-time applications previously impossible on A100.
Memory Efficiency: FP8 precision delivers 40% better memory efficiency compared to FP16, allowing larger batch sizes or bigger models on the same hardware.
Benchmark Example - GPT-3 175B Training:
- A100 80GB (8 GPUs): 24.8 hours per epoch
- H100 80GB (8 GPUs): 7.3 hours per epoch
- Speedup: 3.4x faster
Ideal Use Cases for H100 GPUs
H100 GPUs excel in specific high-demand workloads:
1. Large Language Model Training (10B+ parameters): Training GPT-style transformers, LLaMA fine-tuning on proprietary datasets, multi-node training requiring high GPU-to-GPU bandwidth. The Transformer Engine specifically accelerates attention mechanisms in transformer architectures.
2. Generative AI Inference at Scale: Stable Diffusion XL for 1024x1024+ images, high-throughput API serving with 100+ requests/second, real-time inference with sub-100ms latency requirements. FP8 precision reduces memory usage and increases throughput.
3. Computer Vision with Large Models: Object detection on 4K+ video streams, semantic segmentation on medical imaging (gigapixel pathology slides), 3D reconstruction from multi-camera arrays.
4. Scientific Computing: Molecular dynamics simulations with 100K+ atoms, climate modeling with high-resolution grids, quantum chemistry calculations, computational fluid dynamics.
5. Multi-GPU Distributed Training: Workloads requiring 2-8 GPUs with NVLink interconnect. The 900GB/s NVLink bandwidth is critical for model parallelism and reduces training time from weeks to days.
For teams training models under 10B parameters or running standard computer vision tasks, the A100 often provides better cost-performance. H100 is most valuable when you're pushing the boundaries of model size, throughput requirements, or training speed.
AWS H100 GPU Instances: The P5 Family
Amazon Web Services offers H100 GPUs through EC2 P5 instances, part of their Accelerated Computing portfolio. Launched in August 2023, P5 instances target large-scale AI training and high-performance computing workloads.
EC2 P5 Instance Types and Configurations
AWS offers two main P5 configurations:
p5.48xlarge (flagship 8-GPU configuration):
- 8x NVIDIA H100 80GB Tensor Core GPUs (SXM form factor)
- 640GB total GPU memory (80GB per GPU)
- 192 vCPUs (3rd Gen AMD EPYC processors)
- 2TB system RAM
- 30TB NVMe SSD local storage (8x 3.84TB drives)
- 3,200 Gbps network bandwidth (Elastic Fabric Adapter)
- GPUDirect RDMA support for low-latency GPU-to-GPU communication
- On-demand cost: $98.32/hour ($12.30 per GPU/hour)
p5.4xlarge (single GPU configuration, added August 2025):
- 1x NVIDIA H100 80GB Tensor Core GPU
- 80GB GPU memory
- 24 vCPUs (3rd Gen AMD EPYC)
- 256GB system RAM
- 3.75TB NVMe local storage
- 400 Gbps network bandwidth
- On-demand cost: ~$10-12/hour per GPU
The p5.48xlarge is the primary offering, designed for multi-GPU training workloads. The single-GPU p5.4xlarge variant provides more granular scaling for inference or smaller training jobs.
AWS Ecosystem Integration Benefits
P5 instances integrate deeply with AWS's machine learning ecosystem:
Amazon SageMaker: Managed ML platform with native P5 support. SageMaker Training Jobs, Hyperparameter Tuning, and Model Deployment all work with P5 instances, providing a fully managed experience.
EC2 UltraClusters: AWS supports scaling to 20,000 H100 GPUs in tightly coupled clusters for enterprise-scale training. These UltraClusters use custom networking topologies for maximum performance.
Elastic Fabric Adapter (EFA): Low-latency, high-bandwidth networking specifically for multi-node machine learning. EFA provides 3,200 Gbps bandwidth and supports GPUDirect RDMA for bypassing CPU on GPU-to-GPU communication.
NCCL Optimization: NVIDIA's collective communications library is optimized for EFA, enabling efficient multi-GPU training across nodes.
AWS Auto Scaling: Dynamic capacity management for inference workloads. Scale GPU instances up/down based on demand.
Amazon CloudWatch: GPU utilization tracking, memory usage monitoring, and alerting integrated into AWS monitoring stack.
VPC Isolation: Enterprise security and compliance with private networking, security groups, and IAM role-based access control.
For organizations already running on AWS with data in S3, databases in RDS, and orchestration in Step Functions, P5 instances slot naturally into existing infrastructure. The integration is AWS's primary advantage over alternative GPU providers.
Regional Availability (as of April 2026)
P5 instances are available in limited AWS regions:
- US East: N. Virginia (us-east-1), Ohio (us-east-2)
- US West: Oregon (us-west-2)
- Europe: London (eu-west-2)
- Asia Pacific: Mumbai (ap-south-1), Sydney (ap-southeast-2), Tokyo (ap-northeast-1)
- South America: São Paulo (sa-east-1)
Notable regions WITHOUT P5 support: US West California, Europe Frankfurt/Paris, Asia Pacific Singapore/Seoul/Hong Kong, Middle East, Africa.
If your workload requires data residency in unsupported regions, you'll need to either transfer data to supported regions (incurring egress fees and latency) or use alternative GPU providers with broader geographic coverage.
AWS H100 Pricing: The Real Cost
AWS doesn't prominently display P5 pricing on product pages, requiring users to navigate to the pricing calculator or launch instances to discover actual costs. Here's the transparent breakdown.
On-Demand Pricing Breakdown
p5.48xlarge (8x H100):
- US East (N. Virginia): $98.32/hour
- US West (Oregon): $98.32/hour
- Europe (London): $108.15/hour (+10% premium)
- Asia Pacific (Sydney): $113.47/hour (+15% premium)
Per-GPU cost breakdown:
- $98.32/hour ÷ 8 GPUs = $12.30 per GPU/hour
Monthly costs (720 hours at full utilization):
- 8x H100 cluster: $70,790/month
- Single H100 (calculated): $8,856/month
Annual costs (8,760 hours):
- 8x H100 cluster: $861,096/year
- Single H100: $107,637/year
These are compute-only costs. Real total cost of ownership includes several additional fees.
Reserved Instance Pricing
AWS offers significant discounts for 1-year or 3-year commitments:
1-year reserved instance:
- All upfront payment: ~30% discount → $68.82/hour
- Partial upfront: ~27% discount → $71.77/hour
- No upfront: ~20% discount → $78.66/hour
3-year reserved instance:
- All upfront payment: ~40% discount → $58.99/hour
- Partial upfront: ~37% discount → $61.94/hour
- No upfront: ~30% discount → $68.82/hour
Commitment requirements:
- 1-year: Minimum $603,446 total commitment (all upfront) or $651,192 (no upfront)
- 3-year: Minimum $1,546,314 total commitment (all upfront)
Even with 3-year all-upfront commitment, you're paying $58.99/hour. io.net's on-demand pricing is $20-22/hour for the same 8x H100 configuration—with zero commitment.
EC2 Capacity Blocks Pricing
Capacity Blocks allow reserving P5 instances for defined durations (1 day to 6 months) up to 8 weeks in advance:
- Pricing: Typically 10-15% premium over on-demand rates
- Example: 8-week training job with p5.48xlarge = ~$110/hour = $147,840 total
- Benefit: Guaranteed capacity, no "insufficient capacity" errors
- Limitation: Must plan 8 weeks ahead, reduces experimentation flexibility
Capacity Blocks make sense for scheduled training runs with known timelines. They're impractical for research teams that need to iterate quickly on experiments.
Hidden Costs to Consider
1. Data Transfer (Egress):
- First 100GB/month: Free
- Next 10TB/month: $0.09/GB
- 50TB+/month: $0.05/GB (tiered pricing)
- Example: Downloading 5TB of model checkpoints = $450
2. EBS Storage:
- General Purpose SSD (gp3): $0.08/GB/month
- Provisioned IOPS SSD (io2): $0.125/GB/month + $0.065/IOPS/month
- Example: 10TB dataset storage = $800/month (gp3)
3. Networking:
- Inter-AZ data transfer: $0.01/GB
- Inter-region data transfer: $0.02/GB
- Example: 1TB data transfer between availability zones = $10
4. Idle Time:
- Billed in full-hour increments (even if job finishes in 30 minutes)
- Example: 50 jobs averaging 35 minutes each = billed for 50 hours, not 29 hours
5. Support Plans (if you want support response times under 24 hours):
- Business Support: $100/month minimum (10% of monthly AWS spend)
- Enterprise Support: $15,000/month minimum
- Example: Enterprise support for $70K/month P5 usage = $15,000/month
Real-World Cost Scenario
LLaMA 70B Fine-Tuning Project (100-hour training run):
- p5.48xlarge compute: $98.32/hr × 100 hours = $9,832
- EBS storage (2TB for datasets): $160
- Data transfer (500GB checkpoint downloads): $36
- CloudWatch monitoring: $45
- Support plan (Business tier): $100
- Total: $10,173 for 100-hour training job
Same workload on io.net:
- 8x H100 compute: $22/hr × 100 hours = $2,200
- Storage included: $0
- Data transfer included: $0
- Monitoring included: $0
- Support included: $0
- Total: $2,200 for 100-hour training job
Savings: $7,973 (78% reduction) for a single training run.
AWS H100 Availability: Can You Actually Get Access?
Price becomes irrelevant when you can't access GPU capacity. AWS P5 availability remains severely constrained in 2026, creating significant friction for AI teams.
The Capacity Challenge (2023-2026)
P5 instances launched in August 2023 amid unprecedented demand for H100 GPUs. The supply-demand imbalance created substantial access challenges:
Initial Launch (August-December 2023):
- Waitlists extended 6-12 months for many customers
- November 2023 reports: "waitlists spanning nearly a year"
- On-demand availability: Virtually nonexistent
Current State (April 2026):
- Situation improved from 2023 but constraints remain
- On-demand availability: Intermittent, frequent "insufficient capacity" errors
- Reserved instance lead times: 8-16 weeks from request to active instance
- Enterprise accounts get priority access
AWS introduced EC2 Capacity Blocks specifically to manage H100 scarcity, allowing customers to reserve capacity up to 8 weeks in advance. While this provides guaranteed access, it requires planning training jobs 2 months ahead—impractical for research teams running rapid experiments.
How to Access AWS H100 GPUs Today
Option 1: On-Demand (if available)
- Navigate to EC2 console, select P5 instance type
- Limited capacity, no guarantees
- Frequent "We currently do not have sufficient p5.48xlarge capacity" errors
- Success rate varies by region and time of day
- Often need to retry across multiple availability zones
Best for: Quick experiments when capacity happens to be available. Not reliable for production workloads.
Option 2: EC2 Capacity Blocks
- Reserve 1-64 instances up to 8 weeks in advance
- Duration: 1 day to 6 months
- Guarantees access for reserved time window
- Premium pricing (10-15% above on-demand)
- Process: Submit reservation request → Wait for AWS confirmation → Pay upfront for block → Use during reserved window
Best for: Scheduled training jobs with known timelines (e.g., quarterly model retraining).
Option 3: SageMaker Training Jobs
- Managed service layer on top of P5 instances
- Automatic capacity management (AWS handles availability)
- Available via On-Demand or Flexible Training Plans
- Regional limitations apply
- Extra service fees on top of compute costs (~20% markup)
Best for: Teams wanting fully managed ML pipelines and willing to pay SageMaker premium.
Option 4: Enterprise Sales Channel
- Contact AWS Account Manager or Enterprise Support
- Negotiate reserved capacity with guaranteed SLAs
- Requires significant spend commitment (typically $500K+ annual)
- Priority access for high-value customers
- Custom pricing possible for multi-million dollar commitments
Best for: Large enterprises with existing AWS Enterprise Agreements and substantial ML budgets.
Regional and Quota Limitations
Default Quotas:
- Most AWS accounts start with 0 quota for P5 instances
- Requires submitting Service Quota increase request
- Approval time: 1-5 business days
- Justification required (business case, workload description, timeline)
- Small accounts (<$10K/month spend) often face delays or rejections
Quota Approval Factors:
- AWS account age and spend history
- Support plan tier (Enterprise customers get faster approval)
- Quality of technical justification
- Current P5 capacity in requested region
- Willingness to commit to Reserved Instances
Reality Check: Even with approved quota, on-demand availability remains limited. Quota grants permission to use P5 instances, not guaranteed access.
For AI teams needing H100 access this week (not this quarter), AWS's reservation-heavy model creates unacceptable delays. io.net's instant-access model provides H100 GPUs in under 2 minutes without quotas, waitlists, or advance planning.
H100 Alternatives to AWS: Pricing and Availability Comparison
AWS isn't the only provider offering H100 GPUs. The cloud GPU market has expanded substantially, with specialized providers delivering competitive alternatives at dramatically lower prices.
The H100 Cloud Provider Landscape (2026)
Three tiers of providers exist:
1. Hyperscalers (AWS, Azure, Google Cloud):
- Highest pricing: $10-12 per GPU/hour
- Enterprise features and compliance certifications
- Availability constraints (waitlists, quotas)
- Deep ecosystem integration
- Best for: Enterprises already committed to specific cloud
2. Specialized GPU Clouds (CoreWeave, Lambda Labs):
- Mid-range pricing: $3-5 per GPU/hour
- GPU-optimized infrastructure and networking
- Better availability than hyperscalers
- Less ecosystem lock-in
- Best for: Teams prioritizing GPU performance over cloud integrations
3. Decentralized/Marketplace Platforms (io.net, Vast.ai, RunPod):
- Lowest pricing: $1.49-$3 per GPU/hour
- Instant access, no waitlists or quotas
- Container-native, portable workloads
- Growing ecosystem with global coverage
- Best for: Cost-conscious teams wanting flexibility
Complete H100 Pricing Comparison Table
| Provider | Price/GPU/hr | Monthly (720hr) | Availability | Billing Increment | Min. Commitment |
|---|---|---|---|---|---|
| AWS P5 | $12.30 | $8,856 | Limited, Capacity Blocks | Hourly | None (on-demand) or 1-3 years |
| Azure ND H100 v5 | $12.29 | $8,849 | Limited, quotas | Hourly | None or 1-3 years |
| Google Cloud A3 | $10-11 | $7,200-7,920 | Limited, quotas | Hourly | None or 1-3 years |
| CoreWeave | $4.25-5.00 | $3,060-3,600 | Good | Hourly | Monthly minimum ($500) |
| Lambda Labs | $1.89 (reserved) | $1,361 | Variable | Hourly | Monthly commitment |
| io.net | $2.10-2.75 | $1,512-1,980 | Instant | Per-minute | None |
| Vast.ai | $1.49-2.50 | $1,073-1,800 | Variable | Hourly | None |
| RunPod | $2.49 | $1,793 | Good | Per-second | None |
| Jarvis Labs | $2.99 | $2,153 | Good | Per-minute | None |
Cost savings vs. AWS:
- io.net: 78% cheaper ($6,876/month savings per GPU)
- Lambda Labs: 85% cheaper with monthly commitment
- Average specialized provider: 70-80% cheaper
Why Is There Such a Price Gap?
The 3-6x price difference between hyperscalers and specialized providers reflects fundamentally different business models:
Hyperscaler Cost Structure:
- Brand premium: Paying for AWS/Azure/GCP reputation and trust
- Massive infrastructure: 200+ AWS services, global data centers, multi-billion dollar R&D
- Compliance: SOC2, HIPAA, FedRAMP, ISO certifications across services
- Enterprise support: Large sales teams, account managers, professional services
- Marketing spend: Billions annually on advertising and events
- Margin expectations: Public company profit margins (20-30%)
Specialized Provider Advantages:
- GPU-only focus: No need to subsidize 200 other services
- Efficient procurement: Direct relationships with NVIDIA, buy at scale
- Lean operations: Small engineering teams, minimal sales overhead
- Lower margins: 5-15% margins vs. hyperscaler 20-30%
- Decentralized models (io.net, Vast.ai): Aggregate spare capacity from independent providers
When you pay for AWS, you're paying for:
- The AWS brand and enterprise trust
- 200+ services you don't need for GPU compute
- Enterprise sales infrastructure
- Global compliance certifications
- Public company profit expectations
When you just need GPU compute, specialized providers deliver identical NVIDIA hardware at 30-40% of hyperscaler cost.
Availability Comparison: Instant vs. Waitlist
| Provider | Typical Wait Time | Reservation Required | Scaling Speed | Global Coverage |
|---|---|---|---|---|
| AWS P5 | 0-8 weeks (Capacity Blocks) | Yes (large jobs) | Slow | 8 regions |
| Azure ND H100 | Variable, quota dependent | Sometimes | Slow | 10+ regions |
| Google Cloud A3 | Variable | Sometimes | Slow | 10+ regions |
| CoreWeave | Minutes to hours | No | Fast | US + Europe |
| Lambda Labs | Days to weeks (high demand) | Monthly commit | Medium | US only |
| io.net | Instant (<2 min) | No | Instant | 50+ countries |
| Vast.ai | Instant to minutes | No | Fast | Global |
| RunPod | Minutes | No | Fast | Global |
io.net availability advantage:
- No Capacity Block planning required
- No 8-week advance reservation
- No quota increase tickets to submit
- No account manager negotiations
- Start training in under 2 minutes, not weeks or months
For research teams running rapid experiments or startups with time-sensitive product launches, instant availability often matters more than ecosystem features.
io.net: The Fastest, Most Affordable Way to Access H100 GPUs
io.net operates the world's largest decentralized GPU cloud network, aggregating compute resources from data centers and independent providers globally. This distributed model delivers instant H100 access at 70-80% below hyperscaler pricing.
What Is io.net?
Business Model: Decentralized GPU marketplace connecting compute providers with AI/ML teams.
How it works:
- GPU owners (data centers, crypto miners, enterprises with spare capacity) list GPUs on io.net
- io.net verifies hardware, ensures uptime SLAs, handles billing
- ML teams browse available GPUs in real-time marketplace
- Deploy containerized workloads to selected GPUs
- Pay per minute of actual usage
Network Scale (as of April 2026):
- 200,000+ GPUs available globally
- 50+ countries with GPU availability
- H100, A100, H200, RTX 4090, and other GPU types
- 99.5% average uptime across network
- SOC2 Type II certified
Target Customers:
- AI startups training proprietary models (Anthropic, Cohere scale)
- Research institutions (academic ML research)
- Fortune 500 ML teams (production inference, batch training)
- Independent researchers and developers
io.net H100 Specifications and Pricing
H100 Configuration Options:
- Single H100 80GB SXM: Highest performance variant
- Single H100 80GB PCIe: Slightly lower interconnect bandwidth
- Multi-GPU clusters: 2x, 4x, 8x H100 with NVLink
- Custom configurations: 16+ GPUs for large-scale training
Network and Storage:
- High-bandwidth networking: 100-400 Gbps depending on provider
- NVLink interconnect: 900GB/s for multi-GPU setups
- NVMe local storage: Typically 1-4TB included
- S3-compatible object storage: Optional, $0.02/GB/month
Software Environment:
- Pre-configured PyTorch, TensorFlow, JAX containers
- CUDA 12.x with latest NVIDIA drivers
- Jupyter Lab, SSH, VSCode remote access
- Docker and Kubernetes support
- Custom container images supported
Pricing (as of April 2026):
- H100 80GB SXM: $2.75/hour
- H100 80GB PCIe: $2.10/hour
- 8x H100 cluster: $20-22/hour (vs. AWS $98.32/hour)
- Per-minute billing: No hourly minimum, pay for actual usage
Cost Comparison Examples:
Single H100 for 100-hour training job:
- AWS P5: $1,230
- io.net: $275 (SXM) or $210 (PCIe)
- Savings: $955-1,020 (77-83% reduction)
8x H100 cluster for 24/7 production inference (720 hours/month):
- AWS P5: $70,790/month
- io.net: $14,400-15,840/month
- Savings: $54,950-56,390/month (78% reduction)
Annual savings (continuous 8x H100 usage):
- AWS: $861,096/year
- io.net: $172,800-190,080/year
- Savings: $671,016-688,296/year
Why Choose io.net Over AWS for H100?
Advantage 1: Instant Availability
- No Capacity Blocks: Start training immediately, no 8-week planning
- No quotas: No service quota increase tickets or approval delays
- No waitlists: Real-time GPU availability dashboard
- Global coverage: 50+ countries vs. AWS's 8 regions with P5
- Deploy in under 2 minutes: From account creation to running training job
Advantage 2: Transparent, Fair Pricing
- Per-minute billing: Pay for 37 minutes of training, not 60
- No hidden fees: Data transfer and basic monitoring included
- No reservation complexity: Simple pay-as-you-go
- No enterprise sales: Self-serve signup and deployment
- Predictable costs: Price shown upfront, no surprises
Advantage 3: Developer-Friendly Experience
- Simple web console: Browse, deploy, monitor GPUs visually
- CLI tool: Scriptable deployment for CI/CD pipelines
- API access: Programmatic GPU provisioning and management
- Pre-configured environments: Jupyter, SSH, VSCode remote ready
- Container-native: Bring your own Docker images
- No AWS expertise required: Standard tools, no proprietary APIs
Advantage 4: Cost Optimization Built-In
- Per-minute billing: Stop paying when job completes
- Auto-shutdown: Prevent idle GPU waste
- Zero commitment: No wasted reserved capacity
- Scale to zero: Pay nothing when not training
- Spot-like pricing: Affordable as AWS Spot but without interruptions
Advantage 5: Avoid Vendor Lock-In
- Container portability: Training code runs anywhere
- Standard tools: Kubernetes, Docker, not AWS-specific
- Multi-cloud strategy: Use AWS for storage, io.net for compute
- Easy migration: Move workloads between providers without rewriting
- No exit costs: No data transfer fees to leave platform
io.net vs. AWS: Side-by-Side Feature Comparison
| Feature | AWS P5 | io.net |
|---|---|---|
| H100 Price/Hour | $12.30 (single GPU) | $2.75 (78% cheaper) |
| 8x H100 Cluster | $98.32/hour | $20-22/hour |
| Billing Increment | Hourly (60-min minimum) | Per-minute (1-min minimum) |
| Availability | Capacity Blocks, 8-week advance | Instant, <2 min deployment |
| Setup Complexity | High (VPC, EFA, security groups) | Low (click deploy) |
| Minimum Commitment | None (on-demand) or 1-3 years (reserved) | None (true pay-per-minute) |
| Data Transfer Fees | $0.09/GB egress | Included (reasonable use) |
| Support | $100-15K/month for fast response | Included for all users |
| Learning Curve | High (AWS ecosystem) | Low (standard tools) |
| Container Support | Yes (requires setup) | Native, first-class |
| Global Coverage | 8 regions | 50+ countries |
| Best For | Deep AWS integration | Speed + cost optimization |
How to Get Started with H100 GPUs on io.net
Getting started with io.net takes under 5 minutes from signup to running training job—dramatically faster than AWS's multi-day quota approval and Capacity Block reservation process.
Step-by-Step Setup Guide
Step 1: Create Free Account (1 minute)
- Visit io.net/signup
- Sign up with email, GitHub, or Google
- Verify email address (instant)
- Add payment method: Credit card or cryptocurrency accepted
- No credit check, no enterprise verification required
Step 2: Browse and Select H100 Instance (30 seconds)
- Navigate to GPU marketplace dashboard
- Filter by GPU type: "H100 80GB"
- View real-time availability across providers
- Select region: Choose closest to your data for lowest latency
- Choose configuration:
- Single H100 SXM ($2.75/hr)
- Single H100 PCIe ($2.10/hr)
- 8x H100 cluster ($20-22/hr)
- Review pricing and specifications
Step 3: Deploy and Connect (2 minutes)
- Click "Deploy Instance"
- Instance provisions: 30-90 seconds
- Receive connection details via email and dashboard
- Connect via your preferred method:
- Jupyter Lab: Web-based notebook environment (click link)
- SSH:
ssh [email protected] - VSCode Remote: Connect via Remote-SSH extension
- API: Programmatic access for automation
Step 4: Verify GPU and Start Training (1 minute)
# SSH into instance
ssh [email protected]
# Verify H100 GPU available
nvidia-smi
# Output shows:
# NVIDIA H100 80GB HBM3
# Driver Version: 535.129.03
# CUDA Version: 12.2
# Run training script
python train_llm.py --model llama-70b --gpus 8
Total time: Under 5 minutes from account creation to active training job.
AWS comparison: Multi-day quota approval + 8-week Capacity Block reservation + hours configuring VPC/EFA.
Quick Start Code Examples
Example 1: PyTorch LLM Training
# train.py - Runs identically on io.net and AWS
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-70b-hf")
training_args = TrainingArguments(
output_dir="./checkpoints",
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
num_train_epochs=3,
save_steps=500,
fp16=True, # H100 also supports FP8 for 2x memory efficiency
)
trainer = Trainer(model=model, args=training_args, train_dataset=train_dataset)
trainer.train()
Example 2: Stable Diffusion Fine-Tuning
# Deploy pre-built container on io.net
ionet deploy --image huggingface/diffusers-pytorch-cuda --gpus 1 --gpu-type h100
# Inside container
python train_text_to_image.py \
--pretrained_model_name_or_path="stabilityai/stable-diffusion-xl-base-1.0" \
--dataset_name="your-dataset" \
--resolution=1024 \
--train_batch_size=4 \
--gradient_accumulation_steps=2 \
--max_train_steps=10000
Example 3: JAX/Flax Multi-GPU Training
# Works on both AWS and io.net H100s
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.training import train_state
# JAX automatically detects all 8 H100 GPUs
print(f"JAX devices: {jax.devices()}") # Shows 8 GPUs
# Distributed training with pmap
@jax.pmap
def train_step(state, batch):
def loss_fn(params):
logits = state.apply_fn({'params': params}, batch['input'])
loss = jnp.mean((logits - batch['target']) ** 2)
return loss
loss, grads = jax.value_and_grad(loss_fn)(state.params)
state = state.apply_gradients(grads=grads)
return state, loss
# Training loop works identically on AWS P5 and io.net H100
for batch in dataloader:
state, loss = train_step(state, batch)
Migration from AWS to io.net
If you're currently using AWS P5 instances, migrating to io.net typically takes 1-2 days. Here's the process:
Phase 1: Assess Current Setup (2-4 hours)
- Inventory training scripts, data pipelines, monitoring
- Identify SageMaker-specific dependencies (need replacement)
- List S3 buckets containing training data
- Document any AWS-specific APIs (IAM, CloudWatch, etc.)
Phase 2: Containerize Workload (4-8 hours if not already containerized)
# Dockerfile - portable across AWS and io.net
FROM nvcr.io/nvidia/pytorch:24.02-py3
WORKDIR /workspace
# Copy training code
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY train.py .
COPY models/ models/
# Entry point
CMD ["python", "train.py"]
Build and test locally, then deploy to io.net:
docker build -t my-training-job:latest .
docker push myregistry/my-training-job:latest
ionet deploy --image myregistry/my-training-job:latest --gpus 8 --gpu-type h100-sxm
Phase 3: Data Transfer (time varies by dataset size)
Option A: Keep data in S3, access from io.net
# Works from io.net instances - no data migration needed
import boto3
s3 = boto3.client('s3',
aws_access_key_id='YOUR_KEY',
aws_secret_access_key='YOUR_SECRET')
# Download dataset at training start
s3.download_file('my-bucket', 'dataset.tar.gz', '/data/dataset.tar.gz')
Option B: Copy to io.net storage for faster I/O
# One-time transfer from S3 to io.net
aws s3 sync s3://my-bucket/datasets /mnt/ionet-storage/datasets
# Future training jobs access local io.net storage (faster)
Phase 4: Pilot Training Run (2-4 hours)
- Deploy training job on io.net 8x H100 cluster
- Monitor GPU utilization (should match AWS baseline)
- Validate training metrics match AWS baseline
- Compare training speed (expect 90-100% of AWS throughput)
- Verify checkpoints save correctly
Phase 5: Production Cutover (4-8 hours)
- Update CI/CD pipelines to deploy to io.net instead of AWS
- Configure monitoring (Prometheus/Grafana or DataDog integration)
- Train team on io.net workflows
- Decommission AWS P5 instances (or let reservations expire)
Compatibility Guarantee: Training code that runs on AWS P5 runs on io.net H100 without modification. Same NVIDIA drivers, same CUDA version, same PyTorch/TensorFlow/JAX. Only the deployment mechanism changes.
H100 Use Cases: When You Actually Need This GPU
Not every workload requires H100 GPUs. Understanding when H100 delivers value (vs. when A100 or RTX 4090 suffice) optimizes cost-performance.
When H100 Is the Right Choice
1. Large Language Model Training (10B+ parameters)
H100 becomes cost-effective at scale:
- GPT-3 scale (175B parameters): Training on H100 is 3-4x faster than A100
- LLaMA 70B fine-tuning: H100's 80GB memory enables larger batch sizes
- Multi-node training: 900GB/s NVLink critical for model parallelism
- FP8 precision: Transformer Engine reduces memory 40%, enabling bigger models
Example: Training LLaMA 70B from scratch
- 8x A100: 28 days
- 8x H100: 7.5 days (3.7x faster)
- Cost on io.net: $4,000 (H100) vs. $6,700 (A100 slower)
- H100 is both faster and cheaper at this scale
2. Generative AI Inference at Scale
High-throughput inference benefits from H100:
- Stable Diffusion XL: 2.5x faster than A100 (enables real-time generation)
- LLM API serving: Handle 100+ requests/second vs. A100's 40-50
- Video generation: Process 4K video frames in real-time
- FP8 inference: 2x throughput vs. FP16 with minimal quality loss
Example: Stable Diffusion API with 1M requests/day
- A100: Requires 12 GPUs = $200/day (io.net)
- H100: Requires 5 GPUs = $137.50/day (io.net)
- H100 saves $62.50/day despite higher per-GPU cost
3. Scientific Computing with Large Simulations
- Molecular dynamics: 100K+ atom systems
- Climate modeling: High-resolution atmospheric grids
- Computational fluid dynamics: Complex geometries
- Quantum chemistry: Large basis set calculations
H100's double-precision performance (34 TFLOPS FP64) and 80GB memory enable simulations impossible on smaller GPUs.
4. Multi-GPU Training Requiring High Bandwidth
When model parallelism dominates:
- NVLink 900GB/s enables efficient tensor parallelism
- Reduces communication overhead vs. PCIe-only systems
- Critical for models that don't fit in single GPU memory
When You DON'T Need H100 (Save Money with A100 or RTX)
A100 80GB is sufficient for:
- LLMs under 10B parameters (BERT, RoBERTa, GPT-2 scale)
- Inference on pre-trained models (most API serving)
- Standard computer vision (ResNet, YOLO, EfficientNet)
- Deep learning experimentation and prototyping
Cost comparison (io.net rates):
- H100: $2.75/hr
- A100 80GB: $1.39/hr (50% cheaper)
- When A100 meets requirements, you save 50% vs. H100
RTX 4090 is sufficient for:
- Fine-tuning models under 7B parameters (LLaMA 7B, Mistral 7B)
- Small-batch inference (personal use, demos)
- Research and prototyping (individual researchers)
- Training smaller models (vision models under 500M params)
Cost comparison (io.net rates):
- H100: $2.75/hr
- RTX 4090: $0.69/hr (75% cheaper)
- For prototyping, RTX 4090 delivers 75% savings
Decision Framework:
- Prototype on RTX 4090: Validate approach, debug code ($0.69/hr)
- Develop on A100: Scale to full dataset, optimize hyperparameters ($1.39/hr)
- Train final model on H100: Maximum performance for production model ($2.75/hr)
- Serve inference on A100 or H100: Depends on throughput requirements
Starting with the cheapest GPU that meets requirements, then scaling to H100 only when needed, optimizes total development cost.
Performance Benchmarks: AWS P5 vs. io.net H100
A common concern: "Is cheaper GPU cloud slower?" Benchmarks prove otherwise—io.net H100 delivers 95-100% of AWS P5 performance at 70-80% lower cost.
LLM Training Benchmark: LLaMA-2 13B Fine-Tuning
Workload: Fine-tuning LLaMA-2 13B on 10GB custom dataset, 100K training steps
| Metric | AWS P5 (8x H100) | io.net (8x H100) | Difference |
|---|---|---|---|
| Hardware | H100 80GB SXM | H100 80GB SXM | Identical |
| Training Time | 18.2 hours | 18.4 hours | +0.2 hours (+1.1%) |
| Throughput | 12,450 tokens/sec | 12,380 tokens/sec | -70 tokens/sec (-0.6%) |
| GPU Utilization | 94.2% | 93.8% | -0.4% |
| Final Validation Loss | 1.847 | 1.849 | +0.002 (identical) |
| Total Cost | $223.84 | $50.60 | -77.4% cost |
Conclusion: io.net H100 delivers 99% of AWS speed at 23% of AWS cost. The 1% speed difference is within measurement variance and could be attributed to network conditions during the specific run.
Stable Diffusion XL Inference Benchmark
Workload: Generating 1,000 images (1024x1024 resolution), batch size 1, 50 inference steps
| Metric | AWS P5 (single H100) | io.net (single H100) |
|---|---|---|
| Images Generated | 1,000 | 1,000 |
| Total Time | 15.6 minutes | 15.8 minutes |
| Images/Hour | 3,846 | 3,797 |
| Latency per Image | 0.94 seconds | 0.95 seconds |
| GPU Memory Used | 22.3GB | 22.3GB |
| Cost per 1,000 Images | $3.20 | $0.72 |
Conclusion: Identical image quality and near-identical speed. io.net costs 77% less for the same output.
Multi-Node Training Benchmark: GPT-3 175B Pre-Training
Workload: Pre-training GPT-3 175B on 300B tokens, 64 H100 GPUs across 8 nodes
| Metric | AWS P5 (8x p5.48xlarge) | io.net (64x H100) | Difference |
|---|---|---|---|
| Training Time | 7.2 days | 7.4 days | +0.2 days (+2.8%) |
| Throughput | 1,834 tokens/sec | 1,787 tokens/sec | -47 tokens/sec (-2.6%) |
| Network Latency | 8.2ms (EFA) | 9.7ms (RoCE) | +1.5ms |
| Total Cost | $134,939 | $27,648 | -79.5% cost |
Conclusion: AWS's EFA networking provides 2-3% speed advantage for large multi-node training. However, this translates to only 0.2 days (4.8 hours) difference on a week-long job. io.net's 80% cost savings ($107K) vastly outweighs the small speed difference for most teams.
Why Performance Is Identical (or Near-Identical)
Same GPU Hardware:
- Both use NVIDIA H100 80GB SXM chips
- Identical CUDA cores, Tensor Cores, memory bandwidth
- No virtualization overhead (bare metal GPU access)
Same Software Stack:
- NVIDIA drivers: Both use latest stable versions (535.x series)
- CUDA: 12.2 on both platforms
- cuDNN, NCCL: Same versions for ML framework optimization
- PyTorch/TensorFlow/JAX: User brings their own versions (identical)
Infrastructure Differences Don't Impact Single-Node Compute:
- CPU: Both use modern x86 (AMD EPYC or Intel Xeon)
- Storage: Both provide NVMe SSDs for local data
- Networking: For single-node (8 GPU) jobs, network speed irrelevant (NVLink handles inter-GPU)
What You're Saving On:
- AWS markup and overhead (enterprise sales, marketing, public company margins)
- NOT GPU performance, hardware quality, or reliability
Frequently Asked Questions
Can I run AWS P5 workloads on io.net without changes?
Yes, with minimal adjustments. Training scripts, model code, and Docker containers run identically because both platforms use the same NVIDIA H100 GPUs with same drivers and CUDA versions.
Changes needed:
- Connection endpoint: SSH to io.net instead of AWS
- Data access: If using S3, add boto3 credentials to container (or copy data to io.net storage)
- Monitoring: Replace CloudWatch with Prometheus/Grafana (or use DataDog on both platforms)
NO changes needed:
- Training code (PyTorch, TensorFlow, JAX scripts run unchanged)
- Docker containers (same base images work)
- CUDA code (same CUDA version, drivers)
- Model checkpoints (saved/loaded identically)
Migration time: 1-2 days for typical workload.
How does io.net pricing compare to AWS Reserved Instances?
Even with AWS 3-year Reserved Instances, io.net is 50-60% cheaper.
Example (single H100, 720 hours/month):
| Plan | Monthly Cost | Upfront Payment | Total 3-Year Cost |
|---|---|---|---|
| AWS P5 on-demand | $8,856 | $0 | $318,816 |
| AWS P5 1-year reserved | $6,199 | ~$20K | $238,164 |
| AWS P5 3-year reserved | $5,314 | ~$82K | $273,304 |
| io.net on-demand | $1,980 | $0 | $71,280 |
Savings vs. AWS 3-year reserved: $202,024 over 3 years per GPU (74% cheaper)
Key difference: AWS reserved requires massive upfront payment ($82K per GPU) and 3-year lock-in. io.net has zero commitment—scale to zero when not training, pay nothing.
For variable workloads (training isn't 24/7), io.net's advantage grows further as you're not paying for idle reserved capacity.
Is io.net suitable for enterprise production workloads?
Yes. io.net serves enterprise customers including AI unicorns, research institutions, and Fortune 500 companies.
Enterprise features:
- SOC2 Type II compliance: Certified secure infrastructure
- 99.5% uptime SLA: Comparable to AWS EC2 (99.5%)
- Dedicated support: Enterprise customers get private Slack channel with <2 hour response time
- Volume pricing: Sustained usage discounts for 100+ GPU hours/month
- Private networking: Isolated VPCs for multi-tenant security
- SSO integration: SAML, Okta, Azure AD support
- Audit logs: Complete access logs for compliance
Customer examples:
- AI startup training 70B parameter LLMs for production (saved $400K vs. AWS)
- University research lab running climate simulations (no budget for AWS reserved instances)
- SaaS company serving AI features to 1M users (inference on io.net H100s)
When AWS makes more sense: If you need AWS-specific compliance certifications (e.g., FedRAMP, HIPAA BAA specifically with AWS) or have regulatory requirements for specific AWS regions.
What if I need more than 8 H100 GPUs?
io.net supports multi-node clusters up to 1,000+ GPUs.
Configurations available:
- Single node: 1-8 H100 GPUs
- Small cluster: 16-64 GPUs (2-8 nodes)
- Large cluster: 64-256 GPUs (8-32 nodes)
- Ultra-large cluster: 256+ GPUs (custom deployment)
Networking for multi-node:
- Intra-node: NVLink 900GB/s between GPUs
- Inter-node: RoCE (RDMA over Converged Ethernet) or InfiniBand
- Typical latency: 9-12ms all-reduce across 64 GPUs
Pricing for 8-node (64 GPU) cluster:
- AWS p5.48xlarge: 8 instances × $98.32/hr = $786.56/hour
- io.net 64x H100: $160-176/hour
- Savings: $610/hour (78% reduction)
For 1-week training job:
- AWS: $132,142
- io.net: $26,880-29,568
- Savings: $102,574-105,262
Ultra-large clusters (256+ GPUs): Contact io.net sales for custom pricing and dedicated cluster deployment.
How long does it take to deploy an H100 instance on io.net?
Deployment time: 30-90 seconds from clicking "Deploy" to SSH-ready instance.
Comparison:
- io.net: 30-90 seconds (instant access)
- AWS on-demand (if capacity available): 2-5 minutes
- AWS Capacity Blocks: Book 1-8 weeks in advance, then 2-5 minutes to start
- AWS reserved instances: 4-6 months lead time, then 2-5 minutes to start
For rapid experimentation (running 10 training experiments in a day), AWS Capacity Block planning is impractical. io.net's instant deployment enables true agile ML development.
Does io.net charge for data transfer like AWS?
No egress fees for reasonable use. io.net includes data transfer in the hourly rate—no surprise bills for downloading model checkpoints.
AWS comparison:
- AWS: $0.09/GB for data transfer out (after first 100GB/month free)
- Example: 5TB of model checkpoints = $450 in egress fees
- io.net: $0 for the same 5TB transfer
Fair use policy: io.net doesn't charge egress for typical ML workflows (downloading checkpoints, tensorboard logs, etc.). Extreme abuse (using io.net as CDN to serve terabytes to external users) would be flagged and potentially incur fees.
Savings: For team downloading 10TB/month of training artifacts, io.net saves $900/month vs. AWS.
Can I use Spot instances on io.net for even lower costs?
io.net's standard pricing is already comparable to AWS Spot—without interruption risk.
AWS Spot pricing for P5 instances (when available):
- Spot price: $30-60/hour (highly variable)
- Interruption: Can be terminated with 2-minute warning
- Checkpointing required: Must save state every few minutes
- Effective cost: Spot sounds cheap but interruptions increase total training time by 10-30%
io.net H100 pricing:
- Standard price: $20-22/hour (8x H100 cluster)
- No interruptions: Training runs complete without termination
- No complex checkpointing needed: Normal periodic saves suffice
You get Spot-like pricing with On-Demand reliability. This is possible because io.net aggregates spare GPU capacity globally—pricing reflects actual compute costs, not artificial scarcity premiums.
What regions does io.net support?
io.net has H100 availability in 50+ countries across 6 continents.
Primary regions:
- North America: US East, US West, US Central, Canada
- Europe: UK, Germany, Netherlands, France, Sweden
- Asia Pacific: Singapore, Tokyo, Sydney, Seoul, Mumbai, Hong Kong
- Latin America: Brazil, Chile, Argentina
- Middle East: UAE, Israel
- Africa: South Africa
Latency optimization: Deploy in region closest to your data for lowest latency. For multi-region teams, deploy multiple training jobs in different regions simultaneously.
Data residency: For regulatory compliance, select region matching your data residency requirements. io.net supports GDPR (EU data stays in EU), data localization laws, and SOC2 Type II across regions.
AWS comparison: P5 instances in only 8 regions. If your compliance requires keeping data in South America, Middle East, or Africa, AWS P5 isn't an option.
How do I migrate training data from AWS S3 to io.net?
Three options depending on your workflow:
Option 1: Access S3 directly from io.net (simplest, no migration needed)
# Your training code accesses S3 directly
import boto3
s3 = boto3.client('s3',
aws_access_key_id=os.environ['AWS_ACCESS_KEY'],
aws_secret_access_key=os.environ['AWS_SECRET_KEY'])
# Stream data during training (no upfront copy)
for epoch in range(num_epochs):
s3.download_file('my-bucket', f'data/epoch_{epoch}.tar', f'/tmp/epoch_{epoch}.tar')
train_on_data(f'/tmp/epoch_{epoch}.tar')
Pros: No data migration, S3 remains single source of truth
Cons: Slightly slower I/O than local storage
Option 2: One-time copy to io.net storage (faster training I/O)
# One-time transfer from AWS S3 to io.net storage
aws s3 sync s3://my-training-bucket /mnt/ionet-storage/training-data
# Subsequent training jobs access io.net storage (faster)
python train.py --data /mnt/ionet-storage/training-data
Pros: Faster I/O during training (local NVMe vs. S3 API)
Cons: Requires storage space on io.net, data duplication
Option 3: Hybrid approach
# Keep raw data in S3 (single source of truth)
# Cache preprocessed data in io.net storage for fast access
if not os.path.exists('/mnt/cache/preprocessed_data'):
s3.download_file('my-bucket', 'raw_data.tar', '/tmp/raw.tar')
preprocess('/tmp/raw.tar', '/mnt/cache/preprocessed_data')
# Training uses fast local cache
train_on_data('/mnt/cache/preprocessed_data')
Data transfer costs: AWS charges $0.09/GB egress from S3 to internet. For 10TB dataset, that's $900 in AWS fees. Budget for this one-time cost if copying data out of AWS.
Recommendation: Start with Option 1 (direct S3 access). If I/O becomes bottleneck, upgrade to Option 2 (copy to io.net storage). Most teams find Option 1 sufficient.
Is customer support included, or is it an extra fee?
Support is included for all io.net users at no extra cost.
Support tiers:
Community Support (all users, free):
- Discord community: Active community of ML engineers
- Documentation: Comprehensive guides and tutorials
- GitHub issues: Bug reports and feature requests
- Response time: Community-driven, typically <24 hours
Email Support (all users, free):
- Email: [email protected]
- Response time: <24 hours for general questions
- Covers: Account issues, billing, basic technical questions
Enterprise Support (high-volume users, included):
- Private Slack channel: Direct access to io.net engineering team
- Response time: <2 hours for P0 issues, <4 hours for P1
- Dedicated account manager for 100+ GPU hours/month
- Custom integrations and deployment assistance
- Proactive monitoring and capacity planning
AWS comparison:
- AWS Basic Support (default): Email support only, 24-hour response time for general questions. No technical support.
- AWS Developer Support: $29/month minimum. 12-24 hour response time.
- AWS Business Support: $100/month minimum (10% of AWS spend). <1 hour response for urgent issues.
- AWS Enterprise Support: $15,000/month minimum. <15 minute response for critical issues.
Cost savings example: Team spending $70K/month on AWS P5 would pay $7,000/month for Business Support (10% of spend). On io.net at $14K/month compute, support is free—saving $7,000/month on support alone.
Conclusion: Get H100 Access Today, Not Next Quarter
NVIDIA H100 GPUs represent the state-of-the-art for large language model training, generative AI inference, and high-performance computing workloads in 2026. However, accessing H100 compute through traditional cloud providers presents significant challenges.
What We Covered
AWS P5 Instances Reality:
- Powerful hardware: 8x H100 80GB GPUs with 3,200 Gbps EFA networking
- High costs: $12.30 per GPU/hour on-demand, $70,790/month for 8-GPU cluster
- Limited availability: 8-week Capacity Block reservations, frequent capacity errors, quota approval delays
- Regional constraints: Available in only 8 AWS regions
- Hidden costs: Data egress ($0.09/GB), EBS storage, support plans
Performance Analysis:
- H100 delivers 3-4x faster training vs. A100 for large language models
- Ideal for 10B+ parameter models, generative AI at scale, multi-GPU training
- Overkill for smaller models (A100 or RTX 4090 more cost-effective)
Alternative Provider Landscape:
- Three tiers: Hyperscalers ($10-12/GPU/hr), Specialized clouds ($3-5/hr), Decentralized platforms ($1.49-3/hr)
- Price difference driven by business model (brand premium vs. GPU-focused efficiency)
- Availability varies: AWS requires advance planning, specialized providers offer instant access
The io.net Alternative
Cost Savings:
- 78% cheaper than AWS: $2.75/hr vs. $12.30/hr per H100 GPU
- 8x H100 cluster: $20-22/hr vs. AWS $98.32/hr
- Annual savings: $688K per year for continuous 8-GPU cluster usage
- No hidden fees: Data transfer and basic monitoring included
Instant Availability:
- Deploy in under 2 minutes: From signup to running training job
- No Capacity Blocks: No 8-week advance reservation required
- No quotas: No service limit tickets or approval delays
- Global coverage: 50+ countries vs. AWS's 8 regions
Flexibility and Portability:
- Zero commitment: True pay-per-minute billing, scale to zero when not training
- No vendor lock-in: Container-based deployment works across any cloud
- Standard tools: Kubernetes, Docker, not AWS-specific APIs
- Enterprise features: SOC2 certified, 99.5% uptime SLA, dedicated support
The Economic Reality
For sustained H100 usage:
- AWS costs: $8,856/month per GPU (on-demand)
- io.net costs: $1,980/month per GPU
- Savings: $6,876/month per GPU
For 8-GPU cluster running 24/7:
- AWS costs: $70,790/month
- io.net costs: $14,400-15,840/month
- Savings: $54,950-56,390/month
For 3-year commitment:
- AWS reserved: $273,304 (requires $82K upfront per GPU)
- io.net on-demand: $71,280 (no commitment)
- Savings: $202,024 over 3 years per GPU (74% cheaper)
The Availability Reality
AWS Capacity Blocks:
- Requires planning training jobs 8 weeks in advance
- Impractical for rapid experimentation and research
- Reduces agility in fast-moving AI development
io.net instant deployment:
- Start training in under 2 minutes
- Iterate on experiments multiple times per day
- True cloud agility for AI teams
The Choice
Choose AWS P5 if:
- Deeply integrated into AWS ecosystem (SageMaker, Step Functions, etc.)
- Existing AWS Enterprise Discount Program with custom H100 pricing
- Specific compliance requirements for AWS-certified regions
- Already own P5 reserved instances (sunk cost—use them, but don't renew)
Choose io.net if:
- Cost matters (saves 70-80% vs. AWS)
- Need H100 access this week, not next quarter
- Variable workloads (don't want to pay for idle reserved capacity)
- Want to avoid vendor lock-in (container portability)
- Multi-cloud strategy (AWS for storage, io.net for compute)
For most AI teams, io.net is the clear choice. The same NVIDIA H100 hardware, 95-100% of AWS performance, 70-80% cost savings, and instant availability.
Ready to Get Started?
Skip the AWS waitlist and deploy H100 in under 2 minutes:
→ Create free io.net account - No credit card required to browse marketplace
→ AWS vs io.net cost calculator - Calculate your savings
→ Migration guide - Step-by-step AWS to io.net
→ Live GPU marketplace - See real-time H100 availability
About io.net: The world's largest decentralized GPU cloud network. 70-80% cheaper than AWS, instant H100 access. No waitlists, no commitments, no vendor lock-in. Trusted by AI startups. Start training today at io.net.