Redefining AI Tokenomics

Runara is a platform that intelligently optimizes LLMs and agents, orchestrates across heterogeneous compute, and deploys across any GPU, TPU or NPU.

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San Jose, California

7.5× lower cost and high performance, proven across models from 30 billion to 3 trillion parameters, on any hardware.

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THE PROBLEM AND THE OPPORTUNITY

Workloads and compute are increasingly mismatched

Three things keep changing faster than any team can tune by hand.

AI workloads

New models, multiplying agentic tasks.

AI infrastructure

New chips, memory and bandwidth limits.

Engineering talent

Hard to hire, already stretched.

All of it lands in the same place: cost.

Underused compute

Higher cost per token

What's needed: a hardware-agnostic control plane that redefines the economics.

WHY CUSTOMERS CHOOSE RUNARA

Your model, your cloud, your data.

Runara runs inside your environment, on the hardware you already have, and never asks you to move a model or hand over data.

See the measured proof

Try the Analyser

Lower cost per token - token processing costs down, no extra headcount to get there.

The decision flow runs itself - Profile, quantize, optimize memory, route. No manual tuning.

Any silicon you already have - NVIDIA, AMD, TPU, LPU, trailing edge and new vendor.

Compute you already paid for - Puts under-used hardware to work, particularly trailing edge.

FAQ'S

Got a quick question?

We're here to help you make the right decision. Explore our frequently asked questions and find answers below.

Which models and hardware does Runara support today?

The evaluation covers 2-3 models across 3-4 xPUs, live today. Published benchmarks so far cover gpt-oss-120b, Qwen3.8 176B Flash and MiniMax-M3 428B, measured on trailing-edge GPUs against published current- and latest-generation baselines. You can also point the analyzer at any model on Hugging Face and we'll run the analysis on it - that's a paid run. Broader model, precision and engine coverage is Phase 2; emerging accelerators are Phase 3. Full hardware and rate detail available on request.

Does quantization change my model's output?

Do I need an ML or DevOps team to run this?

Where do my model and data live?

How do we start?

How were the benchmark numbers produced?

THE FULL PICTURE

The numbers are available on request

Our measured benchmarks, the full product detail and a calculator you can run against your own spend. Tell us who you are and we'll send you in.

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I have access

Runs on any silicon,
deployed in one click.

Your model, your cloud, your data. Run the analyzer against your own workloads and see what your current fleet is leaving on the table.

Runs on any silicon,
deployed in one click.

Your model, your cloud, your data. Run the analyzer against your own workloads and see what your current fleet is leaving on the table.

Runs on any silicon,
deployed in one click.

Your model, your cloud, your data. Run the analyzer against your own workloads and see what your current fleet is leaving on the table.

Redefining AI Tokenomics

Workloads and compute are increasingly mismatched

Workloads and compute are increasingly mismatched

AI workloads

AI infrastructure

Engineering talent

All of it lands in the same place: cost.

All of it lands in the same place: cost.

What's needed: a hardware-agnostic control plane that redefines the economics.

Your model, your cloud, your data.

Your model, your cloud, your data.

Got a quick question?

The numbers are available on request

Runs on any silicon, deployed in one click.

Runs on any silicon, deployed in one click.

Runs on any silicon, deployed in one click.

Runs on any silicon,
deployed in one click.

Runs on any silicon,
deployed in one click.

Runs on any silicon,
deployed in one click.