ParallelIQ is a model-aware control-plane company building the next generation of infrastructure for AI and LLM workloads.
We sit above Kubernetes and Slurm — bringing intent, policy, economics, and observability together to make GPU-native AI systems efficient, governable, and scalable.
We believe the future of AI isn’t just smarter models — it’s infrastructure that understands the models it runs.
🏗 What We Build
- 📐 Open standards for declarative AI model intent (ModelSpec / UMIR)
- 🧭 Control-plane workflows for admission, placement, and lifecycle management
- 🔍 Runtime introspection tools for GPU fleets (utilization, MFU, fragmentation)
- 💰 Economic visibility for GPUaaS providers (idle, stranded, underutilized GPUs)
- ⚡ Optimization of GPU efficiency, KV cache behavior, and serving performance
- 🛡 Governance, policy enforcement, and operational guardrails for AI systems
🔍 piqc — PIQC Fact Collector
Kubernetes scanner that discovers LLMs running on vLLM and extracts their deployment and runtime facts.
A Kubernetes-native introspection CLI that automatically discovers vLLM inference deployments, collects GPU metrics, runtime telemetry, and generates standardized ModelSpec documentation. Built for ML Eng, MLOps, and SRE teams.
Python · Kubernetes · vLLM · GPU Metrics · ModelSpec
📄 modelspec — Open Declarative AI Model Specification
An open, declarative specification for describing how AI models are deployed, served, and operated in production.
ModelSpec captures execution, serving, and orchestration intent — making implicit assumptions explicit in a machine-readable, human-auditable format. Runtime-agnostic. Cloud-agnostic. Built for real-world teams.
JSON Schema · LLM Deployment · AI Infrastructure · MLOps · Observability
📚 piqc-knowledge-base — Production Readiness Standards for GenAI
Production-ready checklists and frameworks for deploying LLMs, GenAI models, and AI infrastructure.
A neutral, community-driven collection of deployment checklists, infrastructure best practices, runtime diagnostics, and governance frameworks. Covers vLLM, Kubernetes, GPU optimization, observability, compliance, and Day-0 to Day-2 operations.
AI Infrastructure · vLLM · Kubernetes · GPU Optimization · AI Governance
📨 Business Inquiries: sam@paralleliq.ai
👤 Founder & CEO: Sam Hosseini
🌐 Website: paralleliq.ai
Making AI deployment knowledge open, neutral, and accessible to everyone.
ParallelIQ