GPU Cloud Platform for AI, ML and HPC Workloads.
Accelerate your AI pipelines on Neysa Velocis, a next generation GPU cloud built for training, inference or fine tuning workloads without vendor lock ins, provisioning delays or unpredictable costs.
Unmatched Performance
Boost training and inference cycles with ultra-low latency and high throughput.
Predictable Costs
Transparent pricing with usage-based billing and no hidden fees.
Flexible Architecture
Spin up GPU instances in seconds, scale horizontally or vertically.
Enterprise Security
SOC 2-ready data centers and encrypted storage.
Versatile Deployment
Options as a Bare metal, Virtual Machine or K8 Containers.
AI Optimized Stack
Pre-configured CUDA, PyTorch, JAX, TensorFlow.
Choose Your Configuration
Build the right GPU setup with flexible GPU, CPU, and memory options.
H100 GPU
Ideal for enterprise-level training and large inference
- 80 GB HBM3
- PCIe and SXM versions available
- Multi instance GPU partitioning
- NVLink high bandwidth interconnect
H200 GPU
Advanced acceleration for foundation model training
- 141 GB bandwidth
- Optimized for AI and ML scaling
- Energy efficient performance
- Native integration with CUDA and PyTorch
L40S GPU
Balanced compute for startups and research
- Native integration with CUDA and PyTorch
- Cost efficient for everyday AI operations
- Enhanced tensor performance
- Cloud ready and scalable in clusters
Why Choose Neysa?
Data Sovereignty
Choose region specific deployments to maintain compliance with data residency policies.
24/7 Technical Support
Always on engineering and cloud support team to help optimize performance and cost.
Proven ROI
Customers report up to thirty times faster model training and significantly lower operational overhead.
Compliance
SOC 2
ISO 27001:2022
ISO 27017:2015
ISO 27018:2019
Trusted by Leading Organizations
The world’s most ambitious AI innovators and research institutions run their workloads on Neysa Velocis.
Ready to Accelerate Your AI Workloads?
Deploy your models on Neysa Velocis, the GPU cloud platform engineered for the future of AI infrastructure. No wait times, no lock-ins.
FAQs
1. What can I run on Neysa Velocis GPU Cloud?
You can run any AI, ML, or HPC workload, including LLM training, fine tuning, inference endpoints, computer vision models, reinforcement learning, simulations, RAG pipelines, and high performance research workloads.
2. Which GPUs does Neysa support?
Neysa Velocis offers H100 NVL, H100 SXM, H200 SXM, L4 and L40S GPUs. Each GPU includes vCPU, RAM, flexible storage options, and networking.
3. How fast can I deploy a GPU instance?
Deployment takes a few seconds. There is no queue or waiting period. Once you select your configuration, your instance can be published and deployed almost instantly from the platform.
4. Do I need long term commitments or contracts?
No, Neysa runs on a usage based billing model. You pay only for what you use. There are no lock-ins or mandatory long term commitments. Both on-demand(OD) and reserved pricing options(MRC) are available.
5. Can I scale workloads across multiple nodes?
Yes, Neysa supports multi node orchestration for training and inference. This includes managed Kubernetes service on platform , MIG options, and NVLink connectivity where applicable.
6. Is my data secure on Neysa?
Yes, all data is stored in encrypted environments with SOC 2 ready controls, continuous monitoring, and region aligned storage. Your data stays within compliant and sovereign cloud boundaries. We are ISO 27001 certified for all our services. We are compliant for SOC2 Type 2 standards, ISO 27017 (Cloud Security) and ISO 27018 (Cloud Privacy) certifications.
7. Do you provide enterprise grade support?
Yes, Neysa provides 24 x 7 engineering and cloud support. The team helps with optimization, cost planning, orchestration, and GPU usage tuning.
8. Can I migrate from my current cloud provider?
Yes, migration is simple. You can recreate environments using pre configured CUDA drivers, frameworks, and templates for PyTorch, TensorFlow, and JAX. The Neysa team also assists with full migration planning.
9. Do I need to manage drivers and dependencies?
No, everything is pre configured on the GPU instance. CUDA versions, Nvidia drivers, and key AI frameworks come installed. You can start training or inference immediately.
10. How is pricing structured?
Pricing is transparent and usage based. You pick a GPU type, select a configuration and you pay only for the compute hours used. There are no hidden fees. No additional charges for data ingress, egress, or inference transactions. No “surprise costs” tied to storage I/O operations or API calls.
11. Can startups and research teams use Neysa?
Yes, Neysa offers flexible and cost-effective GPU Cloud instances for research, experimentation, and agile development keeping the needs of startups in mind. Many small teams use these configurations for early stage AI projects. Neysa also has a Neysa AI Velocity Program that gives startups a predictable path to scale, without the post-credit shock as with hyperscalers.
12. Which regions and zones do you support?
With Neysa’s data residency controls, your data never leaves your selected region—ensuring compliance with industry, regulatory, and sovereign data requirements while maintaining complete operational control.
13. Does Neysa support Kubernetes workloads?
Yes, you can deploy GPU powered Kubernetes clusters instantly on the Neysa Velocis platform. The platform includes automated provisioning for containers and pods.
14. How many GPUs can I attach to one instance?
While using Kubernetes cluster, you can autoscale depending on your workload. H100 and H200 clusters support advanced scaling for large models and multi node training.
15. Do you support fine tuning of large language models?
Yes, you can fine tune any LLM the diverse set of GPUs available on Neysa’s GPU cloud.
16. Can I run inference endpoints at scale?
Yes, Neysa supports high throughput inference with ultra low latency due to its specialized AI-infrastructure. You can deploy your endpoint in minutes.