What Are Hyperscalers?
Hyperscalers are the giants of cloud computing — companies that design, build, and operate massive, global-scale data center infrastructures capable of scaling horizontally almost without limit. The term “hyperscale” refers to architectures that can efficiently handle extremely large and rapidly growing workloads, including AI training, inference, and data processing.
Examples:
- Amazon Web Services (AWS)
- Microsoft Azure
- Google Cloud Platform (GCP)
- Alibaba Cloud
- Oracle Cloud Infrastructure (OCI) (smaller but sometimes included)
These companies have multi-billion-dollar capital expenditures (CAPEX) in data centers, networking, and custom hardware (e.g., AWS Inferentia, Google TPU, Azure Maia).
What Are Traditional AI Compute Cloud Providers?
These are smaller or more specialized providers that focus specifically on AI workloads—especially training and fine-tuning large models—often offering GPU or accelerator access, high-bandwidth networking, and lower latency setups.
Examples:
- CoreWeave
- Lambda Labs (Lambda Cloud)
- Vast.ai
- RunPod, Paperspace, FluidStack, etc.
They often use NVIDIA GPUs (H100, A100, RTX 4090, etc.) and emphasize cost-efficiency, flexibility, or performance for ML engineers and researchers.
Key Comparison: Hyperscalers vs. AI Compute Cloud Providers
| Dimension | Hyperscalers | AI Compute Cloud Providers |
|---|---|---|
| Scale & Reach | Global, thousands of data centers; integrated with enterprise ecosystems | Smaller scale, often regional or specialized |
| Hardware | Custom silicon (TPUs, Inferentia, Trainium) + NVIDIA GPUs | Almost entirely NVIDIA GPU-based |
| Pricing Model | Complex, pay-as-you-go; optimized for enterprise commitments (e.g., reserved instances, savings plans) | Simpler, often cheaper hourly or spot pricing; more transparent GPU pricing |
| Performance Focus | Balance of general-purpose and AI-specific workloads | Focused almost entirely on deep learning performance |
| Networking | Proprietary, very high bandwidth and reliability | Can vary; some are optimized for high interconnect (e.g., NVLink, InfiniBand) |
| Ecosystem & Integration | Tight integration with DevOps, databases, storage, analytics, security, identity | Lightweight—focused mainly on compute, with minimal frills |
| Flexibility | Broad platform: supports everything from web hosting to LLM training | Narrow focus: mainly training, inference, and fine-tuning workloads |
| Target Users | Large enterprises, governments, global-scale AI projects | Startups, researchers, model trainers, boutique AI labs |
| Innovation Cycle | Slower—must maintain enterprise stability | Faster—can pivot quickly to support new GPUs or AI frameworks |
| Example Use Case | Multi-cloud enterprise AI strategy integrating ERP + data lakes + model deployment | Cost-effective fine-tuning or inference serving for startups |
How They’re Converging
There’s an emerging hybrid trend:
- Hyperscalers are building specialized AI infrastructure (e.g., NVIDIA DGX Cloud partnerships, Microsoft–OpenAI collaboration, Google DeepMind integration).
- Smaller AI compute providers are adding orchestration layers (e.g., API management, distributed training schedulers) to resemble mini hyperscalers.
Some mid-tier players like Oracle, IBM Cloud, and Tencent Cloud are positioning themselves between these two worlds — offering both enterprise reliability and AI specialization.
In Summary
- Hyperscalers = scale, reliability, and enterprise integration; ideal for end-to-end AI systems.
- AI Compute Clouds = agility, affordability, and specialization; ideal for developers or teams training and serving models directly.
Would you like me to include a comparison of cost-performance ratios for AI workloads (e.g., GPT model training on A100s vs TPUs vs custom silicon)? That can show where each platform has an advantage.
Some Emergent Hyperscalers
- Nscale — a vertically-integrated AI data-centre / GPU cloud scale-play (rapidly expanding via large GPU supply deals and data-centre buildouts). nscale.com+1
- CoreWeave — GPU-first cloud operator focused on ML/graphics workloads; positions itself as lower-cost, fast access to new NVIDIA hardware. CoreWeave
- Lambda Labs (Lambda Cloud) — ML-first cloud and appliances for researchers and enterprises; early to H100/HGX and sells private clusters. lambda.ai
- Vast.ai — a marketplace/aggregator that connects buyers to third-party GPU providers for low-cost, on-demand GPU rentals. Vast AI
- RunPod — developer-friendly, pay-as-you-go GPU pods and serverless inference/fine-tuning; emphasizes per-second billing and broad GPU options. Runpod+1
- Paperspace (Gradient / DigitalOcean partnership) — easy UX for ML workflows, managed notebook/cluster services; targets researchers and smaller teams. paperspace.com+1
- FluidStack — builds and operates large GPU clusters / AI infrastructure for enterprises; touts low cost and large cluster deliveries (recent colocation/HPC deals). fluidstack.io+1
- Nebius — full-stack AI cloud aiming at hyperscale enterprise contracts (recent large Microsoft capacity agreements and public listing activity). Nebius+1
- Iris Energy (IREN) — originally a bitcoin miner now pivoting to GPU colocation / AI cloud (scaling GPU fleet and data-centre capacity). Data Center Dynamics+1
Comparison table
| Provider | Business model | Typical hardware | Pricing model | Typical customers | Notable strength / recent news |
|---|---|---|---|---|---|
| Nscale | Build-own-operate AI data centres + sell GPU capacity | NVIDIA GB/B-class & other datacentre GPUs (mass GPU allocations) | Enterprise deals / reservations + cloud access | Large enterprises, cloud partners | Large GPU supply deals with Microsoft; fast expansion. nscale.com+1 |
| CoreWeave | Purpose-built GPU cloud operator | Latest NVIDIA GPUs (A100/H100, etc.) | On-demand, reserved; claims competitive price/perf | ML teams, render farms, game studios | ML-focused architecture, early access to new GPUs. CoreWeave |
| Lambda Labs | ML-focused cloud + private on-prem appliances | A100/H100/HGX offerings; turnkey clusters | On-demand + private cluster contracts | Researchers, enterprises needing private clusters | Early H100/HGX on-demand; private “caged” clusters. lambda.ai |
| Vast.ai | Marketplace / broker — spot / community & datacenter providers | Varies (user-supplied & datacenter GPUs) | Market pricing / spot-style auctions — often cheapest | Hobbyists, researchers, cost-sensitive teams | Highly price-competitive via marketplace model. Vast AI |
| RunPod | On-demand pods, serverless inference & dev UX | Wide range: H100, A100, RTX 40xx, etc. | Per-second billing, pay-as-you-go | Individual devs, startups, ML teams experimenting | Per-second billing, fast spin-up, developer tooling. Runpod+1 |
| Paperspace | Managed ML platform (Gradient), notebooks, VMs | H100/A100 and consumer GPUs via partners | Subscription tiers + hourly GPU rates | Students, researchers, startups | Easiest UX for notebooks + learning resources. paperspace.com+1 |
| FluidStack | Large-scale cluster operator & managed AI infra | Large fleets of datacenter GPUs | Custom / enterprise pricing (claims big cost savings) | Labs, enterprises training frontier models | Big colocation/HPC deals; expanding capacity via mining/colocation partners. fluidstack.io+1 |
| Nebius | Full-stack AI cloud (aims at hyperscale) | NVIDIA datacenter GPUs (scale focus) | Enterprise contracts / cloud offerings | Enterprises chasing hyperscale AI capacity | Large multi-year capacity deals (e.g., Microsoft). Nebius+1 |
| Iris Energy (IREN) | Data-centre owner / ex-miner pivoting to AI cloud | Building GPU capacity (B300/GB300, etc.) alongside ASICs | Colocation + AI cloud contracts / asset monetisation | Enterprises, HPC customers; also investor community | Pivot from bitcoin mining to GPU/AI colocation and cloud. Data Center Dynamics+1 |
Practical differences that matter when you pick one
- Business model & reliability
- Marketplace providers (Vast.ai) are great for cheap, experimental runs but carry variability in host reliability and support. Vast AI
- Dedicated GPU clouds (CoreWeave, Lambda, FluidStack, Nebius, Nscale, Iris) provide more predictable SLAs and engineering support for production/federated training. nscale.com+4CoreWeave+4lambda.ai+4
- Access to bleeding-edge hardware
- Lambda and CoreWeave emphasize fast access to the newest NVIDIA stacks (H100, HGX/B200, etc.). Good if you need peak FLOPS. lambda.ai+1
- Pricing predictability vs lowest cost
- RunPod / Vast.ai / Paperspace often win on price for small / short jobs (per-second billing, spot marketplaces). For large, sustained runs, enterprise contracts with Nebius / Nscale / FluidStack or reserved capacity at Lambda/CoreWeave may be more cost-efficient. Runpod+2Vast AI+2
- Scale & strategic partnerships
- Nebius and Nscale are scaling via huge supply agreements and data-centre builds aimed at enterprise contracts (Microsoft news for both). That makes them candidates if you need tens of thousands of GPUs or long-term buying power. Reuters+1
- Operational maturity & support
- CoreWeave, Lambda, and Paperspace have mature dev experience / tooling and are used widely by ML teams. FluidStack and the miner pivots (Iris Energy) are moving fast into HPC/colocation and can offer very large capacity but may require more custom engagement. irisenergy.gcs-web.com+4CoreWeave+4lambda.ai+4
Which should you pick for common scenarios?
Managed notebooks, easy onboarding: Paperspace (Gradient). paperspace.com
Experimentation / cheapest short runs: Vast.ai or RunPod. Vast AI+1
Research / fast access to newest GPUs: Lambda or CoreWeave. lambda.ai+1
Large-scale, enterprise training / long contracts: Nebius, Nscale, FluidStack, or Iris (colocation + committed capacity). Data Center Dynamics+3Reuters+3Reuters+3
Oracle Cloud Infrastructure (OCI) vs Nscale (as of October 19, 2025). I’ll cover: company profiles, business models, core products & hardware, scale & geography, networking/storage, pricing & commercial approach, enterprise features & ecosystem, strengths/weaknesses, risk factors, and recommended fit / use cases. I’ll call out the most important, source-backed facts inline so you can follow up.
OCI vs Nscale
OCI (Oracle Cloud Infrastructure) — Enterprise-grade public cloud from Oracle with a full-stack platform (150+ services), strong emphasis on bare-metal GPU instances, low-latency RDMA networking, and purpose-built AI infrastructure (OCI Supercluster) for very large-scale model training and enterprise workloads. Oracle+1
Nscale — A rapidly-scaling, GPU-focused AI infrastructure company and data-center operator (spinout from mining heritage) that is building hyperscale GPU campuses and selling large blocks of GPU capacity to hyperscalers and cloud partners — recently announced a major multi-year / multi-100k GPU deal with Microsoft and is positioning itself as an AI hyperscaler engine. Reuters+1
1) Business model & target customers
- OCI: Full public cloud operator (IaaS + PaaS + SaaS) selling compute, storage, networking, database, AI services, and enterprise apps to enterprises, large ISVs, governments, and cloud-native teams. OCI competes with AWS/Azure/GCP on breadth and with a particular push on enterprise and large AI workloads. Oracle+1
- Nscale: Data-centre owner / AI infrastructure supplier that builds, owns, and operates GPU campuses and sells/leases capacity (colocation, wholesale blocks, and managed deployments) to hyperscalers and strategic partners (e.g., Microsoft). Nscale’s customers are large cloud/hyperscale buyers and enterprises needing multi-thousand-GPU scale. Reuters+1
Takeaway: OCI is a full cloud platform for a wide range of workloads; Nscale is focused on delivering raw GPU capacity and hyperscale AI facilities to large customers and cloud partners.
2) Scale, footprint & recent milestones
- OCI: Global cloud regions and an enterprise-grade service footprint; OCI advertises support for Supercluster-scale deployments (hundreds of thousands of accelerators per cluster in design) and already offers H100/L40S/A100/AMD MI300X instance families. OCI emphasizes multi-region enterprise availability and managed services. Oracle+1
- Nscale: Growing extremely fast — public reports (October 2025) show Nscale signing an expanded agreement to supply roughly ~200,000 NVIDIA GB300 GPUs to Microsoft across data centers in Europe and the U.S., plus earlier multi-year deals and very large funding rounds to build GW-scale campuses. This positions Nscale as a major new source of hyperscale GPU capacity. (news: Oct 15–17, 2025). Reuters+1
Takeaway: OCI provides a mature, globally distributed cloud platform; Nscale is an emergent, fast-growing specialist whose business is specifically bulking up GPU supply and datacenter capacity for hyperscalers.
3) Hardware & AI infrastructure
- OCI: Provides bare-metal GPU instances (claimed as unique among majors), broad GPU families (NVIDIA H100, A100, L40S, GB200/B200 variants, AMD MI300X), and specialized offerings like the OCI Supercluster (designed to scale to many tens of thousands of accelerators with ultralow-latency RDMA networking). OCI highlights very large local storage per node for checkpointing and RDMA networking with microsecond-level latencies. Oracle+1
- Nscale: Focused on the latest hyperscaler-class silicon (publicly reported deal to supply NVIDIA GB300 / GB-class chips at scale) and on designing campuses with the power/networking needed to host very high-density GPU racks. Nscale’s value prop is enabling massive, contiguous blocks of the newest accelerators for customers who need scale. nscale.com+1
Takeaway: OCI offers a broad, immediately available catalogue of GPU instances inside a full cloud stack (VMs, bare-metal, networking, storage). Nscale promises extremely large, tightly-engineered deployments of the very latest chips (built around wholesale supply deals) — ideal when you need huge contiguous blocks of identical GPUs.
4) Networking, storage, and cluster capabilities
- OCI: Emphasizes ultrafast RDMA cluster networking (very low latency), substantial local NVMe capacity per GPU node for checkpointing and training, and integrated high-performance block/file/object storage for distributed training. OCI’s Supercluster design targets the network and storage patterns of large-scale ML training. Oracle+1
- Nscale: As a data-centre builder, Nscale’s engineering focus is on supplying enough power, cooling, and high-bandwidth infrastructure to run dense GPU deployments at hyperscale. Exact publicly-documented RDMA/InfiniBand topology details will depend on the specific deployment/sale (e.g., Microsoft campus). Data Center Dynamics+1
Takeaway: OCI is explicit about turnkey low-latency cluster networking and storage integrated into a full cloud. Nscale provides the raw site-level infrastructure (power, capacity, racks) which customers — or partner hyperscalers — will integrate with their preferred networking and orchestration stacks.
5) Pricing & commercial model
- OCI: Typical cloud commercial models (pay-as-you-go VMs, bare-metal by the hour, reserved/committed pricing, enterprise contracts). Oracle often positions OCI GPU VMs/bare metal as price-competitive vs AWS/Azure for GPU workloads and offers enterprise purchasing options. Exact on-demand vs reserved comparisons depend on instance type and region. Oracle+1
- Nscale: Business-to-business, large-block commercial contracts (multi-year supply/colocation agreements, reserved capacity). Pricing is negotiated at scale — Nscale’s publicized Microsoft deal is a wholesale/supply/managed capacity arrangement rather than per-hour public cloud list pricing. For organizations that need thousands of GPUs, Nscale will typically offer custom commercial terms. Reuters+1
Takeaway: OCI is priced and packaged for on-demand to enterprise-committed cloud customers; Nscale sells large committed capacity and colocation — better for multi-year, high-volume needs where custom pricing and term structure matter.
6) Ecosystem, integrations & managed services
- OCI: Deep integration with Oracle’s enterprise software (databases, Fusion apps), full platform services (Kubernetes, observability, security), and AI developer tooling. OCI customers benefit from a full-stack cloud ecosystem and enterprise SLAs. Oracle
- Nscale: Ecosystem strategy centers on partnerships with hyperscalers and OEMs (e.g., Dell involvement in recent deals) and with chip vendors (NVIDIA). Nscale’s role is primarily infrastructure supply; customers will typically integrate their own orchestration and cloud stack or rely on partner hyperscalers for higher-level platform services. nscale.com+1
Takeaway: OCI is a one-stop cloud platform. Nscale is infrastructure-first and will rely on partner ecosystems for platform and application services.
7) Strengths & weaknesses (practical lens)
OCI strengths
- Full cloud platform with enterprise services and AI-optimized bare-metal GPUs. Oracle+1
- Designed for low-latency distributed training at scale (Supercluster, RDMA). Oracle
- Broad GPU/accelerator families (NVIDIA + AMD options). Oracle
OCI weaknesses / risks
- Market share and ecosystem mindshare still behind AWS/Azure/GCP in many regions; vendor lock-in concerns for Oracle-centric enterprises.
Nscale strengths
- Ability to deliver huge contiguous GPU volumes (100k–200k+ scale) quickly via supply contracts and purpose-built campuses — attractive to hyperscalers and large cloud partners. Recent publicized Microsoft deal is a major signal. Reuters+1
- Investor & OEM backing that accelerates buildout (Dell, Nokia, others reported). nscale.com
Nscale weaknesses / risks
- New entrant: rapid growth introduces execution risk (power availability, construction timelines, operational maturity). Big deals depend on multi-year delivery and integration with hyperscaler networks. Financial Times+1
8) Risk & due diligence items
If you’re choosing between them (or evaluating using both), check:
- Availability & timeline: OCI instances are available now; Nscale’s large campuses are in active buildout — confirm delivery timelines for GPU blocks you plan to consume. (Nscale’s big deal timelines: deliveries beginning next year in some facilities per press). TechCrunch+1
- Network topology & RDMA: If you need low-latency multi-node training, verify the network fabric (OCI documents RDMA / microsecond latencies; for Nscale verify whether customers get InfiniBand/RDMA within the purchased footprint). Oracle+1
- Commercial terms: Nscale = custom wholesale/colocation contracts; OCI = public cloud, enterprise agreements and committed-use discounts. Get TCO comparisons for sustained runs. Oracle+1
- Operational support & SLAs: OCI provides full cloud SLAs and platform support; Nscale will likely provide data-centre/ops SLAs but may require integration effort depending on the buyer/partner model. Oracle+1
9) Who should pick which?
- Pick OCI if you want: Immediate, production-ready cloud with GPU bare-metal/VM options, integrated platform services (K8s, databases, monitoring), and predictable on-demand/reserved pricing — especially if you value managed services and global regions. Oracle+1
- Pick Nscale if you want: Multi-thousand to multi-hundred-thousand contiguous GPU capacity under a negotiated multi-year/colocation deal (hyperscaler-scale training, or to supply a cloud product), and you can accept a bespoke onboarding/ops model in exchange for potentially lower per-GPU cost at massive scale. (Recent Microsoft deal signals Nscale’s focus and capability). Reuters+1
Short recommendation & practical next steps
- If you’re an enterprise or team needing immediate GPU clusters with full cloud services -> evaluate OCI’s GPU bare-metal and Supercluster options and request price/perf for your model. Use OCI if you want plug-and-play with enterprise services. Oracle+1
- If you are planning hyperscale capacity (thousands→100k GPUs) and want to reduce per-GPU cost through long-term committed deployments -> open commercial discussions with Nscale (and other infrastructure suppliers) now; verify delivery schedule, power, networking fabric, and integration model. Reuters+1