DISTRIBUTED COMPUTE
GPU POOL is a cloud GPU service offering the high-performance GPUs needed for AI training, inference, and scientific computing — in a form that suits corporations and research institutions, with invoicing, quotes, and PoC consultations.
We carry high-performance GPUs across NVIDIA's Blackwell, Hopper, and Ampere generations, and propose the best fit for your use case, budget, and timeline.
We also carry GPUs for inference, validation, and 3DCG rendering.
Discuss a configuration* Available GPU configurations vary by timing and stock. Please contact us for details.
Not sure which GPU fits your use case? Start with a conversation.
Discuss GPU configurationsWe support the GPU workloads AI development and research call for. Inquiries are welcome even before your use case is set.
Tuning Japanese and domain-specific LLMs, with support for mid-training pause/resume and hyperparameter search.
GPU configurations suited to pre-launch inference validation, cost estimation, and comparisons with quantized or distilled models.
For training vision models: visual inspection, medical imaging, autonomous-driving datasets.
For GPU-accelerated research computing: simulation, molecular dynamics, fluid and structural analysis.
As a research GPU environment for universities and institutes, with research-budget consultation, quotes, and invoicing.
Pre-purchase validation before buying your own GPUs, with short-term configurations and support through the PoC.
Even before your use case is set, we will hear out your workload and propose a configuration.
GPU POOL is designed as the answer to the friction of both self-procurement and major clouds.
For training, PoCs, and time-boxed research that don't need 24/7 operation, paying only for the period you need is economical — no idle assets like with self-purchase.
Instead of "just give us 8× H100," we work with you on a configuration that is neither over- nor under-provisioned, based on your workload, model size, and timeline.
Compared with self-procurement, there is no upfront investment in servers, power, cooling, or maintenance — making the decision to start research lighter.
No need for accounting to absorb currency swings — we work in a form that suits internal approval and bookkeeping, with quotes issued in advance.
* Costs depend on configuration, duration, and workload. Rather than advertising flat "up to X% off" figures, we spell out concrete comparison terms in each quote.
Each option has strengths and weaknesses. This table is an honest picture of where GPU POOL fits.
| Aspect | JANCTION GPU POOL | Major cloud GPUs | Self-procured GPUs |
|---|---|---|---|
| Upfront cost | Not required | Not required | Upfront investment in servers, power, cooling |
| Short-term use | Yes (by arrangement) | Yes (usage-based) | Risk of idle assets |
| Cost predictability | Term contracts make budgeting easy | Variable due to usage pricing | Depreciation is predictable |
| Invoicing & quotes | Supported | With conditions | — |
| Configuration consulting | Proposed per use case | Choose from a catalog | Up to you to assess |
| Operational burden | Onboarding support included | Mostly self-service | Operations workload required |
* The comparison shows general tendencies; actual terms depend on each vendor's latest specs and contracts.
Feel free to request a quote compared against your current cloud costs.
Request a switching quoteWe propose the best plan by use case, duration, and configuration. Below are three representative plan types; final pricing is quoted individually.
For short validations & PoCs
For ongoing training & research
For production and large-scale research
* Prices shown are indicative. Final pricing is quoted based on configuration, duration, SLA, and other terms.
Stories from AI development and research teams using GPU POOL.
Signed a monthly dedicated plan for 8× H100. Invoicing and grant support cleared internal approval, and the environment was built three weeks from application.
Procured 4× A100 on a term contract for a training-heavy period, securing throughput without expanding in-house GPUs and keeping fixed costs down.
Signed a 4-week plan for 2× A100. With support preparing approval documents and quotes, internal sign-off went through and validation started with existing data as-is.
From first contact to full use, we proceed through use-case interviews and configuration proposals.
Submit the form or download materials. Tell us roughly your use case, GPU count, and duration — anonymous inquiries are fine.
We ask about use case, model size, duration, and budget, then propose the best GPU configuration and plan.
We provide quotes covering GPU configuration, duration, pricing, and SLA terms — including documents for internal approval and grant applications.
Run a proof of concept on real GPUs and confirm performance and cost before committing.
After contracting, full use begins — with ongoing quoting, billing, and configuration-change support.
Answers to common questions. For anything not covered, contact us directly.
It's fine if use case, duration, or model size aren't decided yet. We will get back to you within two business days.