Compute Comparison

IO.NET

GPU Marketplace

IO.NET is a decentralized GPU network aggregating idle compute from data centers, crypto miners, and consumer hardware — including H100 and A100 — at prices typically well below traditional on-demand GPU cloud providers. The marketplace model enables batch AI inference, LLM training, and distributed workloads at dramatically reduced cost for teams comfortable with variable hardware reliability. A compelling option for crypto-native teams and cost-sensitive developers who can tolerate the trade-offs of a decentralized GPU network.

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Cheapest On-Demand

$0.280/hr

Cheapest Spot

GPU Listings

3

Billing

On-demand

Performance Benchmarks

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Provider Info

Headquarters

San Francisco, CA

Founded

2023

Regions

Various

Min Commitment

None

Support

Community

Strengths

  • Very low prices on H100 and A100
  • Large pool of available GPUs
  • Decentralized resilience
  • Crypto-native billing

Limitations

  • Marketplace model — hardware quality varies by host
  • No enterprise SLAs or uptime guarantees
  • Not suitable for compliance-sensitive workloads

Best For

Batch inferenceCost-sensitive trainingCrypto-native teams

Full GPU Catalog

GPU ModelvRAMOn-DemandSpotAvailabilityRegion
RTX 409024 GB$0.280HighVarious
A100 80GB80 GB$0.840HighVarious
H100 80GB80 GB$1.81MedVarious

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IO.NET GPU pricing overview

IO.NET is a marketplace GPU cloud provider headquartered in San Francisco, CA. IO.NET is a decentralized GPU network aggregating idle compute from data centers, crypto miners, and consumer hardware — including H100 and A100 — at prices typically well below traditional on-demand GPU cloud providers. The marketplace model enables batch AI inference, LLM training, and distributed workloads at dramatically reduced cost for teams comfortable with variable hardware reliability. A compelling option for crypto-native teams and cost-sensitive developers who can tolerate the trade-offs of a decentralized GPU network. Billing is On-demand with a minimum commitment of None. Available regions include Various. On-demand GPU instances can be provisioned in minutes with no upfront cost, making IO.NET suitable for both short-duration experiments and sustained production workloads.

IO.NET vs other GPU providers

IO.NET competes with providers including Lambda Labs, CoreWeave, RunPod, Paperspace, Vast.ai, and the major hyperscalers (AWS, Google Cloud, Azure) for GPU compute workloads spanning LLM training, fine-tuning, and inference serving. Key differentiators include: Very low prices on H100 and A100; Large pool of available GPUs; Decentralized resilience. Use the side-by-side comparison tool above to see IO.NET pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 3 IO.NET listings alongside 94+ providers in a single sortable view.

Best use cases for IO.NET

IO.NET is best suited for: Batch inference, Cost-sensitive training, Crypto-native teams. Support tiers range from Community, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 3 active GPU listings on IO.NET, covering RTX 4090, A100 80GB, H100 80GB. For workloads requiring the highest single-GPU throughput, H100 SXM5 instances with NVLink interconnect deliver the best performance per dollar at scale. For cost-sensitive fine-tuning or inference of models up to 13B parameters, A100 40GB or RTX 4090 instances typically offer the best value.

IO.NET billing model and cost structure

IO.NET uses On-demand pricing. On-demand instances are billed per second or per hour depending on the instance type, with no termination fees. Spot pricing is not currently available on this provider — all instances are on-demand. Reserved instance pricing, where available, can reduce costs by 30–60% for predictable long-running workloads. Always compare the effective hourly rate including egress, storage, and networking costs when evaluating total cost of ownership across providers.

Choosing the right GPU on IO.NET

GPU selection depends on model size, precision, and whether your workload is compute-bound or memory-bandwidth-bound. For LLM training above 30B parameters, H100 80GB SXM5 instances with NVLink are the standard choice — the 3,350 GB/s HBM3 bandwidth and 989 TFLOPS FP16 throughput make them 2–2.5× faster than A100 for transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB offers the best cost-per-token on most providers. RTX 4090 instances are ideal for fine-tuning, prototyping, and quantized inference (INT4/INT8) of models up to 70B. Read the H100 vs A100 guide or the GPU benchmarks for ML guide for a full breakdown.

How IO.NET pricing data is collected

Prices shown are sourced from IO.NET's public pricing API or pricing page and refreshed every 15 minutes. On-demand rates reflect the current list price for a single GPU instance in the cheapest available region. Spot prices, where available, reflect interruptible instance rates at the time of the last snapshot. All prices are in USD per hour. Daily snapshots are retained for 90 days and visualised in the GPU price history charts — useful for identifying seasonal pricing patterns and evaluating whether current rates are above or below the 30-day average.

Evaluating managed LLM inference APIs as an alternative to self-hosted GPU compute? Compare live LLM token prices across OpenAI, Anthropic, Google, Groq, and 14+ other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.

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On-demand from $0.280/hr — 3 GPU configurations available. On-demand billing.

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