Low Latency
CUDA runtime drastically reduces agent response times versus cloud-based inference.
Deploy AI Agents on NVIDIA CUDA Self-Hosted GPU
Run GPU-accelerated AI agents on your own infrastructure for maximum performance, low latency, and absolute data privacy. Take control of your AI deployment stack.
GPU accelerated inference
Self-hosted control
Zero cloud dependency
Unlock Performance
with GPU-Native Execution
Lyzr runs AI agents directly on your NVIDIA CUDA hardware, ensuring data sovereignty, minimizing latency, and delivering enterprise-grade performance without cloud dependencies.
Data Sovereignty
Self-hosted GPU deployment keeps all your sensitive data within your own network.
Cost Efficiency
Own your compute infrastructure and eliminate expensive, unpredictable per-token API costs.
Full Customization
Achieve model-level control with custom runtimes and fine-tuning on your hardware.
Enterprise Scale
Scale workloads across your GPU fleet for unmatched throughput and reliability.
for Enterprise
for Enterprise
Lyzr empowers enterprise teams in regulated industries to run high-throughput AI workloads on their own secure, on-premise NVIDIA GPU infrastructure.
Enterprise AI Ops
Deploy task-specific AI agents on dedicated CUDA GPU clusters for internal use.
Regulated Industries
Meet compliance for finance and healthcare with air-gapped on-premise AI agent deployment.
High-Throughput AI
Power real-time inference and parallel multi-agent workloads with GPU acceleration.
Your infrastructure, your data, and your models. Regain complete control over your enterprise AI agents.
Benefits of Self-Hosted
NVIDIA CUDA Deployment
Maximum Inference Speed
CUDA-optimized execution delivers faster token throughput and lower end-to-end latency.
Complete Data Privacy
No data ever leaves your self-hosted environment, meeting the strictest compliance needs.
Elastic GPU Utilization
Scale agents horizontally across multiple NVIDIA GPUs, avoiding all cloud bottlenecks.
Full Model Ownership
Own your entire model stack—weights, runtime, and agent logic—with no vendor lock-in.
Lyzr's Platform
Capabilities
Our platform provides native CUDA runtime compatibility, agent orchestration, multi-GPU support, and robust deployment tooling for your hardware.
CUDA Integration
Native CUDA driver and runtime compatibility for seamless execution on NVIDIA hardware.
Multi-GPU Orchestration
Lyzr intelligently distributes agent workloads across multiple NVIDIA GPUs for speed.
On-Premise Model Loading
Load local LLMs or fine-tuned models directly onto GPU memory via our agent runtime.
Secure Inference Sandbox
Run agents in an isolated execution environment without exposing your host infrastructure.
Deployment Logs
Get real-time GPU utilization tracking, agent performance logs, and health dashboards.
AI Agent Deployment:
Lyzr vs Alternatives
| Feature | Cloud AI APIs | OSS Frameworks | Lyzr |
|---|---|---|---|
| GPU Infrastructure | No Control | Full control, manual | Full, managed control |
| Data Residency & Privacy | Vendor Controlled | Self-managed privacy | Guaranteed on-premise |
| CUDA-Native Runtime | Black-box, no access | Requires manual build | Optimized & pre-configured |
| Multi-GPU | Not applicable | Complex manual setup | Built-in orchestration |
| Custom Models | Limited or no support | Requires coding | Seamless, simple integration |
| Offline & Air-Gapped Use | Requires internet | Possible | Designed for air-gap |
| Enterprise Grade Security | Depends on vendor | DIY security | Built-in enterprise security |
| Inference Latency | High & variable | Depends on setup | Ultra-low, predictable |
| Scalable Cost Model | Per-token or API call | Only hardware costs | Fixed, predictable pricing |
| Dedicated Support | Tiered, slow | Community-based | Dedicated expert support |
Why Deploy AI with
Lyzr on CUDA?
GPU-Native by Design
Lyzr is built for CUDA hardware, not retrofitted from a cloud-first design.
Enterprise Security
Our on-premise model meets the strictest enterprise and regulatory data security needs.
Rapid Deployment
Deploy AI agents on your GPU infrastructure in hours, not weeks or months.
Dedicated Support
Our engineering team provides dedicated support for your self-hosted GPU deployment.
Trusted by Industry
Leaders in AI
Enterprises with the strictest data requirements trust Lyzr to power their AI agents on secure, self-hosted infrastructure, ensuring performance and compliance.
We had to deploy AI agents on NVIDIA CUDA to meet our data residency requirements. Lyzr was the only platform that allowed us to do this securely and quickly. We reduced agent inference latency by 60% while keeping all of our proprietary financial data on-premise, a critical win for us.
VP of AI · Infrastructure, Global Bank
Zero
Data exfiltration incidents
Deploy AI Agents on NVIDIA
CUDA in 4 Steps
Provision GPU
Setup NVIDIA CUDA drivers and verify hardware compatibility.
Install Lyzr Runtime
Install Lyzr's agent deployment package on your self-hosted GPU server.
Configure Models
Load your LLMs into GPU memory and link them to Lyzr's agent logic.
Deploy & Monitor
Launch agents and enable monitoring dashboards for your GPU workloads.
Deploying AI Agents on
Self-Hosted NVIDIA GPUs
What does it mean to deploy AI agents on NVIDIA CUDA?
It means running AI agents directly on your own NVIDIA GPUs using the CUDA toolkit for parallel processing. This provides significant speed advantages and data control compared to relying on cloud-based services for inference, as all computations happen on your secure, private hardware.
What hardware do I need for self-hosted GPU AI agents?
You'll need a server with one or more NVIDIA GPUs (e.g., A100, H100) compatible with a recent CUDA toolkit version. We also recommend sufficient RAM and fast storage to support your models.
How does CUDA AI inference accelerate agent performance?
CUDA enables massive parallelism, allowing thousands of calculations to run simultaneously on the GPU. This drastically increases throughput and reduces latency for complex AI agent tasks compared to CPU-only systems.
Does Lyzr help deploy AI agents on NVIDIA CUDA for multi-GPU setups?
Yes, Lyzr is designed for multi-GPU environments. Our platform includes built-in agent orchestration that intelligently distributes workloads across all available GPUs, maximizing throughput and ensuring efficient use of your hardware.
What are the privacy benefits of on-premise AI agents?
With on-premise agents, your data never leaves your infrastructure. All processing happens locally, eliminating third-party data access risks. This is essential for meeting compliance standards like GDPR, HIPAA, and for air-gapped environments.
Which agents benefit most from GPU-accelerated deployment?
Agents based on large language models (LLMs), Retrieval-Augmented Generation (RAG) pipelines, and complex reasoning tasks see the most significant performance gains. GPU acceleration is crucial for low-latency responses in these scenarios.
How long does it take to deploy AI agents on NVIDIA CUDA with Lyzr?
With Lyzr's pre-built runtime and orchestration tools, you can deploy AI agents on your NVIDIA CUDA hardware in hours. Building a comparable, stable system from scratch can take engineering teams several months of development and testing.
How is Lyzr's NVIDIA GPU agent runtime different from open source?
Lyzr provides a fully managed, enterprise-grade solution with dedicated support, advanced security features, and built-in monitoring dashboards. Our runtime is continuously optimized for CUDA, saving you significant engineering and maintenance overhead.
What is the TCO of self-hosted AI deployment vs cloud APIs?
Self-hosting involves an initial hardware investment (CapEx) but eliminates unpredictable, ongoing operational costs (OpEx) from per-token API fees. For high-volume workloads, self-hosting on owned GPUs offers a significantly lower Total Cost of Ownership (TCO).
Can I update on-premise AI agents without any downtime?
Yes, Lyzr's runtime supports zero-downtime deployments. You can roll out updated models or agent logic seamlessly, as our platform manages the transition to ensure continuous service availability for your critical AI applications.
Got a use case in mind?
8 weeks from use case to
8 weeks from use case to
agents running in production.
Platform, people and FDEs, all in. Bring your environment. We’ll co-build and stay until it’s
live.