Compute
AI Compute Infrastructure
Access scalable GPU compute resources and edge AI infrastructure to power your local business's AI workloads efficiently.
- per-second billing
- scale to zero
- H100 + H200
- serverless or dedicated
- Per-sec
- Billing
- Pay only for what you run, billed per second. No idle GPU cost, and you scale to zero between jobs.
- 0-50%
- Lower cloud spend
- Right-sized allocation and spot capacity cut typical cloud GPU costs for steady workloads.
- H100+
- On demand
- From on-prem edge to H100 and H200 clusters, provisioned when you need them and released when you do not.
02 / The fleet
GPU compute, on demand
From a single L4 to an H200 cluster, provisioned in code and billed per second. Indicative rates below; spot capacity goes lower.
- H200 SXMTrainLargest memory for big-model training and long-context fine-tunes.
- H100 SXMTrainThe workhorse for distributed training and high-throughput inference.
- A100 SXMTrainProven price-performance for fine-tuning and batch jobs.
- L40SServeCost-efficient inference for mid-size models.
- L4ServeLow-cost, low-latency serving and light vision workloads.
- RTX 4090ServeSpot-priced capacity for dev, batch, and burst inference.
- On-prem edgeEdgeLocal devices for real-time, private inference with no internet dependency.
03 / Two modes
Serve it, or train on it
The same fleet runs both: serverless endpoints that scale with traffic, and dedicated clusters for the heavy training runs.
Serverless inference
- Sub-second cold starts
- Autoscale with traffic, scale to zero
- Per-second billing, no idle cost
- Deploy an endpoint from your code
Dedicated training
- Multi-GPU clusters with InfiniBand
- Checkpointing and resumable jobs
- Reserved or on-demand capacity
- We handle drivers, scheduling, and uptime
04 / What it changes
What the build is designed to do
- 01Reduce AI inference latency with strategically placed edge compute resources
- 02Keep sensitive data on-premise with local AI processing capabilities
- 03Scale compute resources up or down based on actual workload demands
- 04Cut cloud computing costs by 30-50% with optimized resource allocation
- 05Ensure high availability and uptime for mission-critical AI applications
- 06Eliminate the need for in-house infrastructure expertise
05 / Workloads
What teams run on it
A few of the workloads teams put on the fleet.
- 01A local restaurant chain deploys edge AI devices in each kitchen for real-time food quality inspection, processing images locally without internet dependency
- 02A regional healthcare provider sets up HIPAA-compliant on-premise GPU servers to run AI diagnostic models, keeping patient data within their facility
- 03A retail store deploys edge computing for real-time inventory tracking and customer analytics, ensuring sub-second response times even during peak hours
- 04A local data center partners with us to offer AI compute services to nearby businesses, creating a shared GPU resource pool at a fraction of individual costs
08 / FAQs
AI Compute Infrastructure questions
Do I need to buy expensive GPU hardware to run AI?
Not necessarily. We evaluate your specific workload and recommend the most cost-effective approach. Many local businesses can run their AI applications on affordable edge devices or optimized cloud instances rather than expensive dedicated GPUs. For businesses with continuous, high-volume AI processing, dedicated hardware may make sense and we help you select and configure it. For variable or lighter workloads, cloud-based GPU access on a pay-as-you-go basis is often the smartest choice.
What is edge AI and why does it matter for local businesses?
Edge AI refers to running AI models on local devices at or near the point of use, rather than sending data to a remote cloud server for processing. This matters for local businesses because it eliminates internet latency, keeps sensitive data on-site, works even if your internet connection goes down, and reduces ongoing cloud computing costs. For applications like real-time video analysis, point-of-sale recommendations, or kitchen quality control, edge AI delivers the instant responses customers expect.
How do you keep AI infrastructure costs predictable?
We design infrastructure with cost predictability as a core requirement. This includes right-sizing resources to your actual needs rather than over-provisioning, implementing auto-scaling that caps at your budget limits, using reserved cloud instances for baseline workloads, and providing monthly cost monitoring with alerts. Most clients see their AI infrastructure costs stabilize within the first 60 days as we optimize resource allocation based on actual usage patterns.
Can you manage the infrastructure so my team does not have to?
Absolutely. Our managed infrastructure service handles everything from initial setup and configuration to ongoing monitoring, maintenance, security updates, and performance optimization. We provide 24/7 monitoring with automated alerting, handle scaling decisions, manage backups, and provide monthly performance and cost reports. Your team interacts with the AI applications we build on top of the infrastructure without needing to worry about the underlying compute resources.
Turn AI Compute Infrastructure into something your team actually uses.
Name the work you want this to handle. We will map the build, show what is worth doing first, and what it costs. If there is no fit, we will say so.