Founder
Nscale
Nscale provides a vertically integrated AI cloud platform combining dedicated GPU infrastructure, managed Kubernetes and Slurm orchestration, and AI services for training, fine-tuning, and inference. The platform spans the full stack from liquid-cooled data centers and behind-the-meter power to bare-metal NVIDIA GPUs with RDMA-enabled fabrics. Nscale's fine-tuning service is designed to be accessible to developers without ML expertise, starting at $2 credit.
- Artificial Intelligence
- Developer Tools
- Software Only
- Enterprise Software
Updated 12 days ago
Funding
Raised to date
$7.1BRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
across 5 rounds
+32$3.4B · Convertible Note
Announced Sep 2026
Disclosed cumulative: $7B
5/5
RoundDateAmountValuationInvestors
Founders
Product
Problem
Training and deploying AI models requires massive computational resources, specialized infrastructure, and deep technical expertise. Many organizations struggle to access sufficient GPU capacity, manage complex orchestration across distributed systems, and secure reliable power and data center capacity, leading to high costs, operational bottlenecks, and slow development cycles.
Solution
Nscale provides a full-stack AI cloud platform that vertically integrates compute, networking, storage, and managed software to support the entire AI lifecycle. The platform offers dedicated GPU infrastructure, managed Kubernetes and Slurm orchestration, serverless and dedicated inference endpoints, and fine-tuning services for customizing foundation models. Nscale's infrastructure is engineered for AI workloads with bare-metal NVIDIA GPUs, low-latency RDMA-enabled fabrics, and parallel file systems designed to prevent training bottlenecks. The company also operates purpose-built, liquid-cooled data centers with access to low-cost renewable energy, enabling predictable performance and cost efficiency at scale.
Target Audience
Primary customers are enterprises, AI scale-ups, governments, and research organizations that need large-scale GPU compute for training, fine-tuning, and production inference workloads, particularly those with strict sovereignty, security, and compliance requirements.
Features
- Managed Kubernetes Service (NKS) with fast spin-up, multitenancy isolation, and failure recovery for production workloads
- HPC-grade Slurm batch scheduler running on Kubernetes for large-scale GPU training runs with scheduled queues
- Serverless and dedicated inference endpoints on reserved GPUs with integrated observability and monitoring
- Fine-tuning workbench supporting side-by-side comparisons and parameter sweeps without requiring ML expertise
- Prompt Workbench for reproducible, collaborative prompt engineering with versioning and direct endpoint integration
- Bare-metal GPU nodes with no abstraction overhead for peak performance and predictable throughput
- RDMA-enabled parallel file systems and high-IOPS NVMe storage tiered for training and inference workloads
- Low-latency InfiniBand and NVLink-capable fabrics supporting multi-pod/multi-rack topologies for distributed training
- Enterprise IAM with RBAC, SSO (SAML/OIDC), VPC isolation, and tenant-scoped key management for data sovereignty