05 // Why Spheron
Why Rent GPUs on Spheron?
Pick the exact GPU your model needs: B300, H200, H100, A100 and more. Billed per minute, no contracts.
Rent NVIDIA GPUs On-Demand | Cloud GPU Marketplace for B300, B200, H200, H100, A100
One platform, every GPU cloud. Deploy H100s to B300s across multiple providers. On-demand, no lock-in, from a single dashboard.
- GPU models
- 50+
- Uptime SLA
- 99.9%
- Deploy time
- <60s
01 // Customers
Trusted by Industry Leaders
03Pricing
GPU PRICING
Cheapest live on-demand rate per GPU on the Spheron marketplace, with the spot rate below it where one is available. Billed per minute. No commitments.
GPU · Model
Specifications
On-Demand · USD / hr
04Reserved
Need More Than
What's Listed?
We'll Source It.
On-demand by default, reserved pricing when you commit. Tell us the cluster size, hardware and term, and we match you with the right data center partner.
Reserved Capacity
Commit to a duration, lock in availability and better rates
Custom Clusters
8 to 512+ GPUs, specific hardware, InfiniBand configs on request
Supplier Matchmaking
Spheron sources from its certified data center network, negotiates pricing, handles setup
How It Works
- 01
Submit your requirements
Tell us your GPU needs, timeline, and scale
- 02
Spheron sources from certified data centers
We match, negotiate pricing, and handle setup
- 03
Reserved capacity confirmed
Guaranteed availability, ready to deploy
Typical turnaround: 24–48 hours for custom sourcing
02 / Servers
Deploy GPU instances in minutes.
One card or a full 8-GPU server, bare metal or VM. No procurement calls, no setup tickets.
03 / Facilities
Access multiple GPU cloud providers from one account.
Every rack belongs to a vetted Tier 3 or Tier 4 data center partner. Use capacity across 5+ providers without opening five accounts.
04 / Network
Unified GPU billing and cost management.
Data centers across regions, linked into one marketplace. One invoice and one dashboard for all of it.
06 // Why pay more
Why pay more for the same GPU?
Same GPUs, without the hyperscaler premium. Spheron sources from certified data centers, so you pay 40-60% less.
Comparison reflects published H100 on-demand annual rates. Last updated April 2026. Live marketplace pricing on the pricing page.
Transparent pricing. No lock-in.
Unified SLA across all providers. One standard, everywhere.
07 // AI agents
Your AI agent's GPU workstation
The Agent GPU Sandbox gives your AI agent its own GPU VM. Ask for a fine-tune, a benchmark or an eval in plain words, approve the GPU, and your agent does the work on the machine and brings the results back to your chat.
Real GPUs, not a CPU container
H100 to B300 VMs your agent keeps for hours, state and all.
Works from your chat
Claude, Cursor or Codex runs commands, jobs and files for you.
You stay in charge
A preview before it spends, a spend limit and your own SSH key.
Agent sessionConnected to Spheron
You
Fine-tune Qwen3 8B on my support tickets and tell me the eval loss.
Agent
An H100 fits a LoRA run on 8B in about two hours. Here is the deploy preview.
deploy_gpuPreview
- GPU
- 1x H100 SXM 80 GB
- Image
- Ubuntu 22.04, CUDA 12.4
- GPU sandbox
- On
- Price
- Hourly rate shown live
You
Yes, deploy it.
get_sandboxReady
- Instance
- qwen3-lora, running
- Sandbox
- Ready for commands
run_commandDone
$ nvidia-smi --query-gpu=name,memory.total --format=csv NVIDIA H100 80GB HBM3, 81559 MiB $ pip install -U transformers trl peft datasets Successfully installed trl-0.19.1 peft-0.15.2
run_commandRunning in background
$ python train.py --model Qwen/Qwen3-8B --lora-r 16 step 200/1800 loss 1.412 lr 2.0e-4 step 600/1800 loss 1.087 lr 1.8e-4 step 900/1800 loss 0.998 lr 1.5e-4 step 1200/1800 loss 0.931 lr 1.1e-4 step 1500/1800 loss 0.889 lr 0.6e-4 step 1800/1800 loss 0.862 done saved adapter to /root/out/adapter
run_commandDone
$ python eval.py --adapter /root/out/adapter eval_loss 0.842 base 1.371 perplexity 2.32 base 3.94
Agent
Done in 1 h 52 min. Eval loss 0.84, down from 1.37 on the base model. The adapter is saved on the machine. Terminate the H100 now?
You
Yes, terminate it.
terminate_deploymentDone
- Instance
- qwen3-lora, terminated
- GPU time
- 1 h 58 min
08 // Building blocks
The GPU cloud
built for AI training
- Why We're
Building SpheronAI compute shouldn't cost 3x more just because AWS has a bigger logo.
Spheron was built to fix that. We aggregate enterprise-grade GPU capacity from certified data centers worldwide into a single, transparent platform.
No waitlists. No lock-ins. No hidden margins. Just the compute your team needs, ready when you need it.{ Powered by best DC's, optimized for builders }
Standard VM ImagesBoot into images with CUDA, PyTorch and the NVIDIA Container Toolkit ready to go. Skip the driver setup and start training.
Ideal GPU DiscoveryWe aggregate pricing from multiple cloud providers so you can find and choose the best deal for your needs.
Maximize Your AvailabilityYour favorite GPUs, at 99.99% availability.
09 // Who we are
GPU Cloud Marketplace
Spheron pools GPU capacity from data centers worldwide. One dashboard, every provider, no hyperscaler markup.
NVIDIA H100, H200, B200 and A100 at 40 to 60% below hyperscaler pricing. No contracts, no waitlists. Need a custom cluster? We source it for you.
We handle the infrastructure. You focus on building.
10 // Compliance
Compliant & Secure.
ISO 27001, SOC 2 Type I & II and HIPAA certified data center partners, all in Tier 3 or Tier 4 facilities.
HIPAA
ISO 27001
SOC 2 Type I
SOC 2 Type II
11 // FAQ
Frequently asked questions
Answers to the questions teams ask before they deploy on Spheron.
You get both options. You can choose to lease either bare metal or VM instances directly from the dashboard.
Yes. Each machine comes with a dedicated IP address. You'll be able to SSH into your VM or bare metal server with full root access.
Absolutely. It's your VM, you have full root access, so you can run containers or any other workloads as you see fit.
It depends on the provider. Some offer InfiniBand, while others don't. If it's available, it will be clearly mentioned on the dashboard before you lease the machine.
Our data center partners deliver 99.98%+ availability with redundant power and networking. While no infrastructure guarantees 100%, you can expect enterprise-grade uptime across all providers.
Plug-and-play for standard deployments. For 100+ GPU clusters, you get dedicated support via Slack or Discord, plus sourcing assistance for the best rates.
Book a callYes, Spheron accepts both traditional payment methods and stables (USDT and USDC). Enterprise invoicing is also available for larger deployments.
Checked on 8 Sep 2026, Runpod listed a single H100 on-demand at $2.89/hr on its public pricing page. The live Spheron H100 on-demand rate is $2.65/hr. Key differences: Spheron bills per-minute after a 20-minute minimum runtime (no wasted hours), has no long-term contract, and offers InfiniBand-connected multi-GPU clusters up to 8x. Third-party rates move without notice, so check Runpod's page before you commit. For a full side-by-side breakdown of pricing, billing, and GPU availability, see our Runpod alternatives guide.
See Runpod alternatives11 // Get started
Deploy your first GPU in under 60 seconds
Pick a GPU, pick a provider and start building on Spheron. Per-minute billing, no lock-in, across 5+ providers.
