Backed by
Combinator
CombinatorPaste a model. RunInfra benchmarks the options and picks the winner. Deploy it, or own the stack.
Example workloads
Every optimization ends with a result you can inspect and run.
You get a benchmark receipt and a runnable deployment kit. Nothing hidden.
Serving engine
Compared, not assumed
GPU target
Sized to the model
p95 latency
Benchmarked
Throughput
Measured per GPU
VRAM
Checked for fit
Cost
Tracked per run
GPU kernels
Tuned where supported
Deployment kit
Run it or export it
From prompt to a production stack you own
RunInfra compares, tunes, and benchmarks the stack. Deploy or export it.
Describe a Llama 3.1 70B workload in plain English.
RunInfra compares 
SGLang and every other engine your model can run on.
It tunes server tuning where it helps, with no config to hand-write.
Deploy on NVIDIA A100 and pay per million tokens, or export the stack and self-host.
New Session
Optimize Llama-3.1-8B-Instruct on vLLM for cheapest GPU with latency and VRAM checks
Capturing cost-first intent for Llama 3.1 8B on vLLM.
Intake updatedModel, engine, goal
ModelLlama 3.1 8B
EnginevLLM
GoalLow cost, latency checked
Plan drafted3.1s
10 execution phases prepared
Plan readyreview / 10 phases / ~23m
Optimize Llama 3.1 8B on vLLM for cheapest GPU with latency checks
Recommended path: vLLM on L4. Review the plan before execution.
Working...
Deploy on thehardware you choose
Compare real GPU prices and deploy wherever fits. The deployment kit and your own cloud mean no lock-in, no rewrites.
Own the winning stack before you ship
Not a black box. You get the measured stack to run, deploy, or export.
Why owning your AI stack matters
Data privacy and control
Keep sensitive workloads on infrastructure you choose.
Customization
Tune the model, runtime, and GPU to your workload.
Performance ownership
Real tuning, measured, not assumed.
Portability
Run it on our cloud, or export it to yours.
Supported across the stack
Open models, serving engines, GPUs, and the clouds you deploy to. RunInfra supports every layer of the inference stack.
Models
Models: Llama 3.3, Whisper, Qwen-Image, NV-Embed, Parler-TTS, Qwen2.5, Cosmos, Pixtral, EmbeddingGemma, RoBERTa, DeepSeek-V3, Sana, Parakeet, Mistral, Wan 2.1, GTE, Qwen2-VL, MMS-TTS, Qwen3 Reranker, Gemma 2, MusicGen, DeepSeek-VL2, Nemotron, FastPitch, PaliGemma, NV-RerankQA, Qwen2-Audio, Hermes 3, Canary, BERT, Llama 3.2 Vision, Qwen3 Embedding
Engines
Engines: vLLM, SGLang, TensorRT-LLM, vLLM Omni, TEI, Transformers
GPUs
GPUs: L4, A10, L40S, RTX 4090, A100, H100, H200, B200
Clouds
Clouds: RunInfra Cloud, Modal, RunPod
Own the winning stack
The stack you optimize exports as a kit that runs without RunInfra. Your key, your hardware, and a free exit.
Common questions
Can't find what you're looking for? Get in touch
What is RunInfra?
Describe what you want to run. RunInfra picks compatible open models, benchmarks GPUs, tunes the runtime, and gives you a deploy-ready stack.
Deploy your first optimized model, measured before you ship
Describe the goal. RunInfra builds and optimizes the stack.
End-to-end encryption
Isolated GPU infrastructure
Zero data retention
SOC 2 Type II
RunInfraby RightNow© 2026 RunInfra. All rights reserved.
Ask AI about RunInfra
