01
The bill only goes up
Once training becomes routine, rental is a fixed monthly expense. Pay for a year and own nothing at the end of it.
On-premise compute
Put your computenext to your data
Rack-scale 8-GPU systems and desktop supercomputers that keep data inside your network. When jobs run daily and the bill climbs monthly, owning usually beats renting cloud GPUs.
Data never leaves your networkOne-time investment, years of useRemote deployment included
Why on-prem
Renting fits experiments and peaks. When compute becomes the team’s daily routine, the cracks start to show.
02
Your data got heavy
Datasets keep growing. Every upload and download means waiting, and egress fees climb right along with them.
03
Compliance got stricter
Patient data, unpublished results, or export-controlled material — whether data can leave your network is now a question you have to answer.
04
Queues and preemption
Popular GPUs are scarce, and preemptible instances get reclaimed without warning — taking multi-day jobs down with them.
When these add up, there’s one answer: move the compute next to the data.
Use cases
Confidentiality, data governance, and training cost are the three most common reasons teams buy.
Confidentiality: data stays home
Patient records, unpublished results, and proprietary company data — once data leaves your network, you lose track of who touched it. On local hardware, training, inference, and debugging all happen on machines you control, and everything keeps running offline.
Data governance: sensitive data stays inside
HIPAA and CCPA/CPRA in the US, GDPR in the EU — data regulations set clear requirements for where sensitive data lives and whether it can leave. On local hardware, most de-identification and approval steps disappear.
Training cost: buy once, stop paying per run
Cloud bills every training run and every test run. Own the hardware and you can iterate, experiment, and rerun without generating a new compute bill — the more you use it, the better the math gets.
Product lines
The 8-GPU system goes in the machine room; the desktop supercomputer sits by your desk. Choose by team size and space.
Rack server · 8-GPU system
From ~$120,000quoted by configuration
- Delivered as one 8-GPU system, specced to your requirements
- Multi-user sharing and job scheduling
- Standard rack deployment; network multiple units into a cluster
- For labs, corporate machine rooms, and long-running projects
One machine holds a whole team’s compute. CPU, memory, storage, and networking balanced to your workloads — rack it and you have your own small compute center.
Ask about this lineDesktop supercomputer
From $30,000quoted by configuration
- Desk-sized and silent — fits in an office
- Plugs in and runs; no machine room, no ops staff
- Dedicated compute, available whenever you need it
- For labs, offices, and individual researchers
No machine room required — it runs next to your desk. Workstation-class compute in a silent desktop box: you lock up and go home, it keeps running.
Ask about this lineMachine room available · Rack 8-GPU system
Clear compute demand, multi-user sharing and scheduling, rack space on hand, or plans to grow into a cluster.
No machine room · Desktop supercomputer / workstation
Still sizing the demand and want something compact, silent, plug-in, and always on.
GPU models, system specs, and pricing are confirmed during consultation.
Configurations
Rack-scale 8-GPU systems for the machine room; desktop workstations and supercomputers for the desk. Toggle to see each tier.
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Mainstream trainingMost chosen
- GPU
- 8 × A100 80GB
- Total VRAM
- 640GB
- Platform
- Dual Xeon · 512GB memory · NVMe storage
- Best for
- 7B–70B full-parameter fine-tuning and inference — the most common research tier
- Reference price
- From ~$120,000 · quoted by configuration
Frontier LLM
- GPU
- 8 × H100 80GB
- Total VRAM
- 640GB, FP8-capable
- Platform
- Dual EPYC · 1TB memory · NVMe storage
- Best for
- 70B full-parameter training and 10B-scale pretraining; networkable into multi-node clusters
- Reference price
- From ~$120,000 · quoted by configuration
Large-VRAM inference
- GPU
- 8 × H200 141GB
- Total VRAM
- 1,128GB, FP8-capable
- Platform
- Dual Xeon · 512GB memory · NVMe storage
- Best for
- Large-model inference, batch serving, and compliance-sensitive projects
- Reference price
- From ~$120,000 · quoted by configuration
Not sure which tier? Send us your model and data size and we’ll configure to the job.
Model support
Grouped by parameter scale. Everyday image generation, transcription, and internal knowledge-base search run privately on it too.
7B–14B
Qwen, GLM, and Llama class
Fine-tune on a single GPU; use all eight for batch inference and high-concurrency serving.
32B–70B
Qwen 72B, Llama 70B, DeepSeek distills
Full-parameter fine-tuning on eight GPUs, quantized inference on the same box.
100B+ / MoE
DeepSeek-V3 class
Quantized deployment on eight GPUs; training wants a multi-node cluster — we’ll help you plan it.
Bioinformatics & science
ESM-2, AlphaFold, scGPT, MONAI
Protein, single-cell, and medical imaging models are VRAM hogs — they run steadiest on local hardware.
Whether your exact model and data scale run — and how fast — is worth testing before you buy: send us a typical workload and we’ll benchmark it on the real machine.
Cost comparison
Short, exploratory needs stay on cloud. Steady, long-term, sensitive workloads go local.
Cloud GPU rental
Hourly billing — fits short, exploratory jobs
- Billing
- Billed hourly — pay for as long as you use it
- Long-term cost
- A year or two of steady use often adds up to the price of the whole machine
- Data movement
- Big datasets uploaded and downloaded over and over; bandwidth and time both cost
- Reliability
- Subject to preemption, throttling, and shortages
- Privacy & compliance
- Depends on contracts and cloud configuration
On-premise hardware
One-time investment, data stays in-house, built for the long run
- Billing
- One-time price by configuration; training and test reruns are never billed again
- Long-term cost
- Total cost is clearly lower over two to three years — and the machine is an asset you keep
- Data movement
- Data lives on-site; copying between disks is immediate
- Reliability
- Dedicated capacity — long jobs run undisturbed
- Privacy & compliance
- Physically under your control; data never leaves the network
You don’t have to pick just one — we can design a hybrid: on-prem as the base, cloud for burst.
Why buy from us
The risk isn’t the spend — it’s hardware arriving with no one to install it, no one trained to use it, and no one to call when it breaks. We take those jobs on.
01
Selection
We size the configuration to your models, data, and budget — which GPU tier, how much storage — so you overspend on nothing and shortchange nothing.
02
Deployment
OS, drivers, CUDA, containers, scheduling, and storage layout — installed in one pass. Existing environments and data migrate over too.
03
Training
We get your team up to speed on daily use, job submission, and what to check first when something breaks. Hardware only pays off if people actually use it.
04
Support
Once you’re up and running, we’re reachable whenever environment or performance issues come up.
Included service
Remote deployment
When the hardware arrives, our engineers deploy it remotely: unboxing guidance, OS and driver installation, CUDA and container environments, scheduling, data migration, plus a round of user training. All remote, no location limits — no need to hire ops staff for one machine.
- Remote unboxing, acceptance, and hardware checks
- Operating system, driver, and CUDA environment installation
- Container and job scheduling setup
- Migration of existing data and dev environments
- Team training and documentation handoff
- Post-delivery remote support
What users say
After deployment, once real jobs are running, the change usually shows up in these places.
The training queue is gone. Jobs go in overnight, and the results are ready by morning.
Our data never left our own machine room — the IRB approved the protocol in a single pass.
I write code by day, it runs inference by night — it has barely stopped since it arrived.
Tell us about your workloads
Tell us your model, data volume, and budget, and we’ll put together a plan — hardware, environment, training, and support in one package.
No hardware expertise required up front
GPU models and pricing confirmed in the proposal
FAQ
How does billing work when we train and test over and over?
The system is priced once by configuration and owned outright. Training, testing, and endless iterations afterwards carry no per-run or hourly fees — only electricity and network costs. Capacity upgrades are quoted separately.
Local or cloud — how do I decide?
Cloud fits short experiments and elastic peaks. Local fits steady, long-term, data-sensitive, 24/7 workloads. Unsure? Send us your cloud bill and we’ll run the total-cost numbers with you.
We have no machine room or ops staff — can we still run it?
The desktop supercomputer is plug-and-play. For the 8-GPU system we provide remote support; daily use needs no dedicated ops, and issues get a remote response.
What if the upfront investment is too much?
Phase it: start with a desktop supercomputer sized to today’s needs and step up to the full system later.
What about scaling later?
8-GPU systems network together for expansion. The design is sized to current needs with headroom built in.
How do I confirm the performance is enough?
Test before you buy: send us a typical workload, we run it on the actual machine, and you’ll have real training throughput and inference speed before settling on a configuration.
What about warranty and after-sales support?
Warranty terms and remote-support response times are spelled out in the proposal and quote.