Description
RunInfra is a chat-native AI model optimization and infrastructure platform designed for building, benchmarking, optimizing, and deploying supported AI workloads. It simplifies the process of creating AI applications and inference infrastructure by allowing users to describe their desired AI application in natural language. Based on this input, RunInfra selects compatible open-source models, benchmarks GPUs, optimizes supported runtimes and kernels, and deploys the necessary infrastructure with measurable evidence.
The platform supports a wide range of AI models, including vetted Hugging Face models, and offers end-to-end support for LLM serving, speech, embedding, vision, and image generation models. Users can easily deploy pipelines as REST endpoints with a single click, and if a model isn't deployable, RunInfra provides clear feedback on the limitations. This transparency contrasts with closed-source APIs, where users often lack insight into the underlying models and infrastructure.
RunInfra's optimization process involves profiling models across various GPUs, experimenting with quantization, KV cache, and kernel tweaks to achieve the best balance of speed, memory, and cost. The platform is designed to ensure data security, with encryption in transit and at rest, and it adheres to SOC 2 Type II standards. Users can choose between managed hosting or self-hosting options, allowing them to maintain control over their data and infrastructure. With a pay-as-you-go pricing model starting at a $10 minimum top-up, users only pay for what they use, making it a flexible solution for teams looking to optimize and deploy AI models efficiently.
Optimize open models for production's Core Features
Chat-native AI model optimization
Real GPU profiling and benchmarking
OpenAI-compatible API endpoints
Managed and self-hosted deployment paths
Smart multi-model routing
Pipeline versioning and comparison
Evidence-gated deployment
Pay only for what you use
How to use Optimize open models for production?
Describe your AI application: Type what you want to build in natural language.
Model selection: RunInfra picks compatible open-source models based on your description.
Benchmark GPUs: The platform benchmarks available GPUs for optimal performance.
Optimize the pipeline: RunInfra tunes the runtime and prepares a deploy-ready stack.
Deploy the model: Use the one-click deployment feature to create REST endpoints.
Inspect results: Review the benchmark receipt and deployment kit provided.
Refine as needed: Use chat to adjust your pipeline before final deployment.
Optimize open models for production's Use Cases
- AI Application Development
- GPU Benchmarking
- Model Deployment
- Cost Optimization
- Data Security Compliance
