RunInfra
What is RunInfra?
RunInfra is an AI inference optimization and deployment platform that builds production-ready APIs from plain language descriptions of open-source models or full apps. Backed by Y Combinator, it benchmarks various serving engines (like vLLM, SGLang, and TensorRT-LLM) across different NVIDIA GPUs to find the most cost-effective and low-latency configuration. It leverages its automated Forge agent to optimize kernels, quantize models (AWQ, GPTQ, FP8), and handle continuous batching and KV cache tuning. Users can deploy on RunInfra's managed infrastructure with scale-to-zero capabilities or export the fully runnable deployment kit (Dockerfile, configs) to host on their own infrastructure like RunPod, Modal, or local hardware.
How to use RunInfra?
Describe your model workload or paste an open-source model from Hugging Face in plain English. RunInfra's automated system will draft an execution plan, benchmark the model across serving engines and GPU configurations, and optimize it. Once the optimal setup is found, you can deploy it as a fully managed API endpoint or export the containerized deployment kit to self-host on your own hardware.
RunInfra's Core Features
No-config plain language model deployment
Automated serving engine benchmarking (vLLM, SGLang, TensorRT-LLM)
Forge agent for custom CUDA kernel generation and tuning
Multi-format model quantization (AWQ, GPTQ, FP8)
Exportable, open-source stack deployment kits (Dockerfile, yaml)
Scale-to-zero managed hosting with pay-per-million-token pricing
Multi-cloud deployment targeting (RunInfra Cloud, Modal, RunPod, Vast.ai)
RunInfra's Use Cases
Deploying open-source LLMs like Llama 3.3 or Qwen 2.5 with optimized latency and cost
Building real-time multimodal voice pipelines using Whisper and vLLM-Omni
Quantizing large models like Qwen 2.5 Coder 32B to fit onto smaller, cheaper GPUs
Generating a fully transparent and exportable Docker stack for self-hosted enterprise AI workloads
FAQ from RunInfra
What is RunInfra?
How is RunInfra different from using closed-source APIs?
What optimization techniques does RunInfra utilize?
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RunInfra Pricing
Free Credits
Free
$5 in free credits for new accounts to try out optimization and benchmarking.
Core
$100 per month
Includes 105 credits ($1 = 1 credit). Includes quantization (AWQ, GPTQ, FP8), standard GPU access (T4, L4, L40S, A100, H100), managed scale-to-zero OpenAI-compatible endpoints, and exportable deployment kits.
Enterprise
Custom pricing
Includes everything in Core plus self-hosted and custom-GPU deployment, B200/H200 GPU access, audit logs, RBAC, SOC 2 Type II compliance, custom SLAs up to 99.99%, and a dedicated CSM.
Analytic of RunInfra
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