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AI & GPU Infrastructure

Run AI Workloads on the Right Infrastructure

Your AI workload shouldn’t have to squeeze into a generic cloud instance. Summit matches it to the right hardware, dedicated bare metal GPUs, dedicated servers, or private cloud, sized for the way it runs. For heavy training and sustained inference, dedicated NVIDIA H200, H100, and L40S come in well under cloud GPU pricing at scale.

Dedicated GPU server racks in a Summit data center
PicsArt runs dedicated GPU servers at Summit to catch objectionable content uploaded to its image-editing app.
100%
Power and network uptime SLAs
22
Data centers worldwide
6
Continents of coverage
24/7
U.S.-based support and Remote Hands
The Problem

Cloud GPU Pricing Was Built to Meter You

Hourly GPU instances are convenient for a weekend prototype. Run sustained training or production inference and the economics turn against you fast.

Bills Scale Faster Than You Do

Hourly metering looks cheap in a demo. Sustained workloads push the monthly invoice well past the cost of owning the same performance outright.

Capacity You Can’t Count On

Popular instance types sell out for weeks. Spot capacity disappears mid-run, and reservations charge a premium for the privilege of waiting.

Virtualization Taxes Every Watt

Shared hosts and hypervisor layers sit between your model and the silicon. Noisy neighbors and overhead leave measurable performance on the table.

Lock-In by Design

Egress fees, proprietary services, and opaque pricing make it costly to leave once your data and pipelines live inside one provider.

Infrastructure Models

Match the Model to Your Workload

What you need depends on how your workload runs, not only where it lives. Here’s how the common AI workloads map to each Summit option.

Bare Metal, GPU-Enabled

Best for: Large-scale model training and sustained inference that need consistent, high-performance GPU access

Run heavy, always-on AI workloads that need maximum performance and control.

Training for days or weeks: LLMs, computer vision, large datasets
Full GPU performance with no shared resources or variability
Predictable, ongoing workloads where monthly cost matters
Explore Bare-Metal Servers

Dedicated Servers

Best for: Single-node AI workloads, smaller training jobs, and early-stage model development

A solid starting point for AI when you need real hardware, but not at massive scale yet.

Smaller training jobs or fine-tuning existing models
A capable single machine rather than a multi-GPU cluster
Getting started with AI in a clean, dedicated environment
Explore Dedicated Servers

Private Cloud

Best for: Development, testing, and variable AI workloads that benefit from flexible, on-demand resources

Build and test AI when flexibility matters more than constant raw performance.

Experimenting with models or running short-lived workloads
Usage that scales up and down: spin up compute, then shut it off
Multiple workloads running across teams
Explore Private Cloud

Colocation, Customer-Owned GPUs

Best for: Long-term, large-scale AI environments where you invest in your own GPU hardware

For teams going all in on AI: build your own GPU environment, hosted by us.

Building your own GPU clusters for long-term AI use
Full control over hardware, configuration, and compliance
High-utilization workloads that justify owning the infrastructure
Explore Colocation

Mac Mini Hosting

Best for: AI development that requires macOS, Apple Silicon (M-series), or Apple-specific frameworks

For AI work that needs Apple hardware, especially building and testing in the Apple ecosystem.

Models that depend on Apple frameworks like Core ML and Metal
Building AI-enabled iOS and macOS applications
Apple hardware for testing, optimization, or CI/CD pipelines
Lightweight or edge-style inference optimized for Apple Silicon
Explore Mac Hosting
Built for AI

AI Infrastructure and Governance

Running AI takes more than raw compute. You need infrastructure to run the workloads, storage to feed your data pipelines, and governance to keep risk and compliance in check. Summit gives you a secure, scalable foundation for all three, from first deployment through day-to-day operations.

AI Inference

Deploy and scale AI workloads on dedicated infrastructure built for performance, consistency, and predictable cost.

AI Storage

Power your AI pipelines with secure, high-performance storage built for massive datasets, model repositories, and long-term growth.

Compliant AI

Get visibility and control over how AI gets used, with governance, security, and compliance that support responsible adoption.

AI Inference

Serving Models in Production

Big models need serious GPU memory and parallel compute. Leaner ones run fine on modern CPUs or Apple Silicon. Summit runs both on private, managed infrastructure, so you size the hardware to the model instead of paying for capacity you won’t use. See the full breakdown of hardware paths and how to choose on our AI Inference page.

AI Storage

Storage Built for AI in Production

Most teams aren’t training models, they’re deploying them. Summit storage is built for that: fast retrieval, model artifact management, and vector workloads at scale.

Storage Options for AI Workloads

Object Storage

S3-compatible API, a drop-in for existing AI pipelines
Model weight storage and fine-tuned artifact versioning
Training datasets, inference logs, and multimodal data (image, audio, video)
Cost-effective at scale versus public cloud egress pricing

Block Storage

High-throughput block storage for low-latency I/O
Feeds GPU inference nodes without I/O bottlenecks
Vector embedding lookups and active RAG retrieval
Enterprise-grade with redundant paths

Colocation for Storage-Heavy Builds

Bring your own NAS or SAN into Summit data centers
On-premises latency with managed data center operations
Suited for petabyte-scale training infrastructure

Backup and DR for AI Data

Managed backup recovers model checkpoints and fine-tuned weights on demand, with air-gap options
DRaaS runs a full recovery program for mission-critical AI pipelines: testing, monitoring, recovery, and runbooks

Storage Tiers for Deployed AI

Hot Tier

SAN and NVMe

Active inference: embedding lookups, vector search, and real-time model serving.

Warm Tier

Object Storage

Current model versions, RAG knowledge base, and recent inference cache.

Cold Tier

Managed Backup

Prior model versions, training datasets, and audit logs.

Compliant AI

AI Infrastructure Your Compliance Team Will Approve

Privately hosted AI environments built for regulated industries: SOC 2 Type II certified, single-tenant, and backed by an audit trail your compliance team can follow.

How Summit Makes AI Compliance Workable

SOC 2 Type II Certified

The entire managed infrastructure stack is covered under Summit’s certification
Annual third-party audits (AT-101), available on request

Private, Isolated Environments

Single-tenant by design, so your data never touches another customer’s environment
Private cloud for AI workloads with dedicated resources
Air-gapped options for the most sensitive workloads

Data Residency and Sovereignty

Choose the exact region your data lives in
EU-resident data centers to support GDPR data residency
London data center for UK data residency under UK GDPR

Advanced Monitoring and Managed Firewall

24/7 NOC
Managed firewall with policy enforcement
DDoS mitigation and network-level controls

How Summit Maps to Your Frameworks

HIPAA

Private cloud, encryption at rest (AES-256), and audit logging, supported through business associate agreements and shared responsibility frameworks.

SOC 2 Type II

Full coverage under Summit’s existing certification (AT-101), with reports available on request.

GDPR and EU AI Act

EU-resident data centers in Amsterdam, Frankfurt, and Bucharest, with data processing agreements and data that stays within the EU. The same residency and data controls support your obligations as an EU AI Act operator.

PCI DSS

Isolated network segments, managed firewall, and a tokenization-friendly architecture.

Core Use Cases

Who Runs on Summit GPUs

Teams that need consistent GPU performance without paying cloud premiums.
AI & ML Startups
Research & Academia
Computer Vision
LLM & Generative AI
SaaS & Inference APIs
Why Summit

Why Teams Move GPU Workloads to Summit

Dedicated performance, flat pricing, and engineers who pick up the phone. The combination is hard to find from hyperscale clouds.

Predictable Flat Pricing

A clear monthly rate for dedicated hardware that holds steady when a training run goes long. Egress is included on every service, so data transfer never adds to the bill.

Bare Metal Performance

Full access to NVIDIA H200, H100, and L40S. The whole card is yours, free of hypervisor tax and noisy-neighbor contention.

100% Uptime SLAs

Summit infrastructure carries 100% power and network uptime SLAs, so your long-running jobs stay online when it matters.

U.S.-Based Engineers

Support and Remote Hands are U.S.-based. When a training run breaks at 2am, a real engineer answers instead of a ticket queue.

Compliance Built In

SOC 2 Type II (AT-101), with HIPAA and PCI DSS supported through business associate agreements and shared responsibility frameworks.

Global Footprint

22 data centers across 6 continents let you place GPU capacity close to your data, your users, and your latency targets.

How It Works

From Workload to Production in Four Steps

1

Scope Your Workload

Tell us your models, frameworks, and throughput targets. We right-size GPUs, storage, and networking around them.

2

Match the Model

Pick the right fit: bare metal GPU, dedicated server, private cloud, colocation, or Mac, with the storage and network profile to match.

3

Deploy and Onboard

We provision the environment and hand it off ready to run, with U.S.-based engineers on call.

4

Scale and Optimize

Add capacity as workloads grow and tune performance with Remote Hands support whenever you need it.

See What Dedicated GPUs Cost at Scale

Send us your current cloud GPU spend and workload profile. We’ll model it against Summit dedicated infrastructure and show you the difference. Read the cloud exit story.

The Comparison

Cloud GPU Instances vs Summit Dedicated Infrastructure

Cloud GPU Instances
Summit Dedicated Infrastructure
Pricing model
Hourly metering that scales with use
Flat, predictable monthly pricing
Hardware access
Shared and virtualized
Dedicated, single-tenant hardware
Performance
Hypervisor overhead and noisy neighbors
Dedicated resources, tuned for your workload
Capacity
Subject to availability and queues
Reserved and provisioned for you
Workload fit
One-size instance types
Bare metal, dedicated servers, private cloud, or colocation
Data egress
Charged per gigabyte
Free on every service
Support
Tiered ticket queues
U.S.-based engineers and Remote Hands
Uptime SLAs
Varies by provider
100% power and network

We were having trouble with our apps not sending data. We called up Summit, and they spent 2 hours talking us through it. It was a simple command line change and they fixed it. AWS won’t do that.

PicsArt
Common Questions

AI Workload FAQ

Which Summit environment fits my AI workload?

It comes down to how your workload runs. Heavy, always-on training and sustained inference fit bare metal GPUs. Smaller jobs and early development fit dedicated servers. Variable, on-demand work fits private cloud. Large, long-term builds on your own hardware fit colocation, and Apple-framework work fits Mac hosting. Tell us what you’re running and we’ll point you to the right model.

How much can I save compared to cloud GPU instances?

Savings depend on your usage pattern, but sustained training and production inference on dedicated hardware typically run far below equivalent hourly cloud GPU instances at scale. Share your workload and current spend, and we’ll model the comparison for you.

Which NVIDIA GPUs are available?

We currently run NVIDIA H200, H100, and L40S. H200 and H100 fit large-model training and fine-tuning, while L40S is a cost-efficient choice for inference and mixed workloads. Need a specific card we haven’t listed? Tell us what your workload needs and we’ll do our best to make it happen.

Is the hardware dedicated or shared?

It depends on the model you choose. Dedicated bare metal and dedicated servers give you single-tenant hardware with full access and no hypervisor overhead. Private cloud is available when you want flexibility over absolute isolation. We help you match the level of dedication to your workload.

Can I run training, fine-tuning, and inference on the same setup?

Yes. The same dedicated hardware supports training runs, fine-tuning, and low-latency production inference, backed by high-throughput storage and private networking.

How do you handle data security and compliance?

Summit maintains SOC 2 Type II (AT-101) and supports HIPAA and PCI DSS through business associate agreements and shared responsibility frameworks, across 22 data centers on 6 continents.

Who supports the environment if something breaks?

U.S.-based engineers and Remote Hands. Real people answer when you need help, whether that’s a hardware question in the middle of the night or guidance on tuning a training run.

Get Started

Price Your GPU Workload

Tell us about your AI workloads and a Summit engineer will get back to you with options and pricing.