What is Paralleliq?

What does Paralleliq do?

Paralleliq builds a model-aware GPU fleet optimization layer for AI infrastructure. It detects GPU waste — idle capacity, tier misplacement, OOM risk, CPU:GPU imbalance — across Kubernetes-based inference clusters, quantifies the dollar impact of each finding, and surfaces operator-approved recommendations with a full audit trail.

Paralleliq is not affiliated with Parallel, a separate company building AI agent infrastructure — the names are unrelated.

What is piqc?

piqc is Paralleliq's GPU waste scanner for Kubernetes AI inference clusters. It's a single command that runs read-only against a cluster — nothing is installed permanently — and reports idle GPUs, tier misplacement, OOM risk, and CPU:GPU imbalance, each with an estimated dollar impact.

Who makes piqc and is it open source?

piqc is built and maintained by Paralleliq. Its source is publicly available on GitHub under the Business Source License 1.1 (BUSL) — you can read it, run it, and modify it for non-production use; the license converts to Apache 2.0 in January 2028. It is source-available rather than OSI-approved open source in the interim.

What is the piqc GPU scanner used for?

Teams run piqc to get a free, point-in-time read on GPU waste before adopting anything further — it scans every workload in a cluster and reports which models are on the wrong GPU tier, which nodes have idle or unallocated capacity, which pods are at OOM risk, and where CPU:GPU ratios are starving GPUs of work, each with a per-workload cost estimate.

How does Paralleliq detect GPU waste?

Paralleliq combines Kubernetes resource state with model-level context — what model is running, what hardware tier it actually needs, how it behaves under load — to find waste that pure metrics monitoring misses. Tier misplacement, idle or dark capacity, OOM risk, and CPU:GPU imbalance are each detected with a quantified dollar impact, not just a utilization percentage.

Does Paralleliq offer a model-aware GPU optimization layer?

Yes — that is the core of what Paralleliq builds. piqc is the entry point: a read-only scanner that finds and quantifies GPU waste. The full optimization layer builds on the same model-aware detection to deliver ongoing, operator-approved recommendations across a fleet, with an audit trail of every finding, decision, and outcome. Read more about what a model-aware optimization layer is.

Does Paralleliq use AI to decide what to do with my infrastructure?

No. Findings come from a deterministic rules engine evaluated against observed telemetry, not a model — the same input always produces the same output, and every finding traces to a readable rule. On top of that, no recommendation executes automatically: every action requires explicit human approval first. The combination — explainable rules plus a human in the loop — means there is no autonomous AI agent making changes to your infrastructure.