We cut your AI billwith compute youalready own.
7 min from now, your first AI workload runs locally.
Sign up
With your work email.
1 minAdd your first node
Through the admin console.
2 minGet an API key
Change your current API key on one workflow with kibbu’s.
2 minConfigure kibbu with your preferences
Like what models to run and how to show you data.
2 min
What have you got?
18 GB available memory of 24 GB · MacBook Pro M4 Pro
7 of 9 models fit this estimate.
Memory and speed estimates depend on model settings and context length. Speed is a rough single-request figure, not a measurement.
- AlibabaQwen 3 Embedding - 0.6B≈ 0.64 GB needed≈ 299 tok/sFits estimate
- GoogleGemma 4 - Compact≈ 3.43 GB needed≈ 56 tok/sFits estimate
- GoogleGemma 4 - Small≈ 5.34 GB needed≈ 36 tok/sFits estimate
- GoogleGemma 4 - 12B≈ 7.38 GB needed≈ 26 tok/sFits estimate
- MetaMuse Glimmer≈ 16.76 GB needed≈ 9 tok/sLimited headroom
- GoogleGemma 4 - 26B MoE≈ 16.8 GB needed≈ 59 tok/sLimited headroom
Memory and speed estimates depend on model settings and context length. The speed shown is a rough single-request figure derived from memory bandwidth, not a measurement.
Running suitable workloads on machines you already own can reduce the inference you buy from the cloud. Bring your own numbers and we will work through the rest.
Estimate my savingsWe take your data seriously.
Using AI shouldn’t mean giving up control of sensitive information. kibbu helps you decide where your workloads run and how your data is handled.
Zero data retention
We don’t store your prompts or responses. Operational metrics are handled separately.
You decide what reaches the cloud
Choose which workloads can use external providers and which must stay within your environment.
On-prem when you need it
For stricter requirements, deploy kibbu within your own infrastructure.
Questions we get before every rollout.
How does kibbu reduce AI costs?
It runs suitable inference workloads on idle machines your company already owns, so you buy less inference from the cloud. Net savings depend on workload fit, available capacity, kibbu fees and operating costs.
How much can we save?
It depends on your models, usage and hardware. Start with one representative workflow, compare its total cost and results, and use that measurement to estimate the wider opportunity.
Which workloads should we start with?
Recurring, latency-tolerant jobs: document processing, enrichment, embedding batches and background agent steps. Check quality and deadline requirements before moving each one.
Will we need to change models?
Possibly. Local execution uses models supported by kibbu and your hardware. A proprietary cloud model may need to stay with its provider. Evaluate any replacement against your own acceptance criteria.
Can we keep using cloud models?
Yes. Use cloud models for workloads that need them, subject to supported provider integrations and your routing policy. Local execution comes in workflow by workflow.
Do we need new hardware?
Start by assessing what you already own. Macs, Windows and Linux, with or without a GPU. Model size, available memory and job deadlines determine which workloads your fleet can handle.
Does this run on employee laptops?
Only the ones that are idle and plugged in, and only the ones you allow. kibbu watches battery and temperature and steps aside the moment someone sits back down.
Can sensitive workloads stay local?
Choose a deployment and routing configuration that keeps those workloads within your approved environment. Confirm logging, telemetry and fallback behavior as part of the deployment review.
Put your compute toward a smaller AI bill.
Bring one recurring workload. See what it costs to run on kibbu, then decide where to expand.
