Managed OpenAI-compatible API
Use managed GPUs and production inference APIs through Netra Cloud, without operating the underlying GPU infrastructure.
Netra Runtime
Netra Runtime is an AI inference engine for production workloads, available through Netra Cloud's OpenAI-compatible API or as a private deployment on customer-controlled AMD or NVIDIA GPU infrastructure. It helps teams optimize model serving, monitor workloads, and retain control over their data and infrastructure.
AI inference for managed or private GPU environments
Netra Runtime is a high-performance AI inference engine for teams running models in production. It sits between an AI application and GPU infrastructure, optimizing model execution to target lower latency, higher throughput, and more consistent performance at scale.
The product is available in two deployment models: Netra Cloud provides managed GPU inference through an OpenAI-compatible API, while private deployment brings Netra Runtime into an organization's own GPU environment. The site also describes support for open-model serving, private fine-tuning workflows, monitoring, and enterprise deployments supported by Forward Deployed Engineers.
Core capabilities
Private deployment
Deploy the inference runtime directly on private GPU infrastructure so models and data remain within the organization's environment.
Runtime-level optimization
The platform is built to improve inference throughput and latency, helping existing GPUs process more AI workloads and potentially reducing infrastructure cost per token.
Inference monitoring
Track latency, throughput, reliability, and usage as inference workloads scale, providing operational visibility into production serving.
Open-model and private-data workflows
Serve open models on managed GPUs and run models tuned or fine-tuned on private data while keeping datasets and checkpoints private.
Target-specific kernel work
The published Netra Kernel repository provides MIT-licensed, target-specific AMDGPU kernels, validation harnesses, runtime bridges, and SGLang serving integration for supported contracts.
Practical use cases
Managed production inference
Teams can call open models through Netra Cloud's OpenAI-compatible API and start serving through managed GPUs without building their own GPU operations layer.
Private enterprise serving
Organizations with existing AMD or NVIDIA capacity can deploy Netra Runtime alongside their infrastructure to improve serving performance while retaining control of models and data.
Private model development
Model teams can fine-tune models on private datasets while keeping datasets and checkpoints within their controlled workflow, then serve the resulting models in production.
Inference operations and capacity planning
Engineering and operations teams can monitor latency, throughput, reliability, and usage to evaluate and manage AI workloads as demand increases.
Pros and Cons
Pros
- Offers both a managed OpenAI-compatible API and deployment on customer-controlled GPU infrastructure.
- Focuses on production inference concerns including latency, throughput, reliability, usage tracking, and GPU utilization.
- Supports AMD and NVIDIA infrastructure, with published benchmark coverage for AMD Instinct MI350X and NVIDIA H200.
- Provides enterprise private-deployment assistance through Forward Deployed Engineers.
- Publishes open-source, MIT-licensed AMDGPU kernel work for selected inference paths and hardware targets.
Cons
- Support for a particular GPU, model, or deployment configuration should be confirmed with Netra; the public evidence does not provide a complete compatibility matrix.
- Private deployment and enterprise licensing use a contact-led process rather than self-serve checkout.
- Published performance figures are benchmark results for specified models, hardware, workloads, and serving configurations, not universal speed guarantees.
FAQ
What is Netra Runtime?
Netra Runtime is a high-performance inference engine that sits between an AI application and GPU infrastructure. It optimizes model execution for production serving, with a focus on throughput, latency, and GPU utilization.
Does Netra offer a managed API?
Yes. Netra Cloud provides managed inference through an OpenAI-compatible API, so teams can use Netra's inference service without operating their own GPUs.
Can Netra Runtime run on private infrastructure?
Yes. Netra Runtime can be deployed on an organization's own GPU infrastructure, keeping models and data within that environment. Enterprise or private deployment support includes assistance from Forward Deployed Engineers.
Which GPUs does Netra support?
Netra is designed for AMD and NVIDIA GPU infrastructure. Published benchmarks include AMD Instinct MI350X and NVIDIA H200, but support for a specific GPU and model configuration should be confirmed with Netra.
How does Netra Cloud pricing work?
Netra Cloud uses prepaid credits. The pricing page provides model input and output usage costs so teams can estimate spending before adding credits; payments are processed through Doku.
Quick Facts
- Category
- AI inference infrastructure
- Deployment options
- Netra Cloud managed API or private GPU deployment
- API style
- OpenAI-compatible API for Netra Cloud
- GPU platforms
- AMD and NVIDIA; confirm specific model support
- Pricing model
- Prepaid credits for cloud usage; enterprise deployment is contact-led
- Published benchmark
- 81.33K output tokens/s for Qwen3.6-35B on 8× AMD Instinct MI350X in a stated benchmark configuration
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