AI SERVICES

Seamlessly build, tune, and run AI

Deliver advanced AI with confidence using scalable inference endpoints, controlled fine-tuning workflows, and a unified workbench for prompt engineering across teams and environments.

AI from experimentation to production

Move faster without compromise across the AI lifecycle

Inference Endpoints

Deploy and scale production inference with fully managed endpoints.

  • Ship inference in minutes. No clusters, GPUs, or infrastructure to operate
  • Scale from prototype to production with low-latency and high throughput
  • Meet data compliance requirements with strict customer isolation

Fine-Tuning

Customize foundation models to your enterprise data with low-friction fine-tuning.

  • Fine-tune models with your own data to align behavior, accuracy, and outputs
  • Lower the cost and complexity of fine-tuning through a streamlined workflow
  • Move tuned models into production in a repeatable, governed flow

Prompt Workbench

Make prompt engineering reproducible, collaborative, and production-ready.

  • Bring structure to prompt engineering with repeatable experiment runs
  • Reduce trial-and-error cost and time-to-prototype without burning GPU hours
  • Move seamlessly from experimentation to production

AI services built for production

Experiment faster

Accelerate prompt iteration and tuning in a browser workbench with versioning and direct usage with inference endpoints.

Scale with confidence

Run serverless, autoscaling inference on Nscale-managed GPUs with integrated observability and strict data boundaries.

Ship reliably

Combine reproducible prompts and fine-tuning with managed inference, monitoring, and versioning to deliver predictable, production-grade AI at scale.

ACROSS TGE AI LIFECYCLE

Choose your ecosystem

Support every stage of the AI lifecycle, from experimentation and training to fine-tuning, inference, and production. Nscale brings together an open ecosystem of models, frameworks, and tools to build, deploy, and scale AI.

Open by design

Built on open standards with portable workloads and familiar APIs, Nscale gives you the flexibility to build your way, without proprietary tooling or vendor lock-in.

Built for developers

Scale AI efficiently with the tools you already trust. From distributed training with Ray and PyTorch to production inference with vLLM and Hugging Face, Nscale provides the infrastructure to grow with your workloads.

Future ready

Get rapid access to the latest open models, frameworks, and hardware as they become available. Spend less time integrating infrastructure and more time building AI applications.

Power enterprise AI at scale

Telecommunication

Scalable, AI-native infrastructure

Telco companies can leverage Nscale’s GPU infrastructure to deliver AI services, optimize 5G networks, support advanced AI analytics, and drive next-generation telecommunications innovations.

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AI Native

Accelerated AI model deployment

AI-native companies can leverage Nscale’s scalable GPU cluster infrastructure to enhance model development, support critical operations, and drive innovation in their tech solutions.

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The Nscale Production Engine

Access thousands of GPUs tailored to your needs

Reserve GPUs

FAQs

Nscale managed inference leverages cutting-edge GPUs optimized for both batch and streaming workloads. With our integrated software stack and orchestration using Kubernetes and SLURM, we provide unmatched performance, scalability, and efficiency.

Yes, we have a library of popular open source models that you can deploy and use at any time. On top of this, our service supports integration with popular AI frameworks like TensorFlow, PyTorch, and ONNX Runtime, allowing you to seamlessly deploy and use your existing models.

No. We built Nscale Fine-tuning to be simple and accessible by only exposing more advanced settings and parameters if you need them. This service does not require machine learning expertise or infrastructure management and can be started by any developer with $2 credit.

Yes. The fine-tuning workbench supports side-by-side comparisons and parameter sweeps including temperature, max tokens, and chain steps, enabling you to see how model choice and hyperparameters affect outputs and to quickly identify the best configurations.