Stop Wasting GPUs. Start Running AI Like a Business.
Stop Wasting GPUs. Start Running AI Like a Business.
Stop Wasting GPUs. Start Running AI Like a Business.
Stop Wasting GPUs. Start Running AI Like a Business.
You can’t optimize what you can’t see. ParallelIQ gives you complete visibility into your AI workloads, model behavior, and GPU efficiency — so you can scale AI with discipline, reliability, and predictable cost.
You can’t optimize what you can’t see. ParallelIQ gives you complete visibility into your AI workloads, model behavior, and GPU efficiency — so you can scale AI with discipline, reliability, and predictable cost.
You can’t optimize what you can’t see. ParallelIQ gives you complete visibility into your AI workloads, model behavior, and GPU efficiency — so you can scale AI with discipline, reliability, and predictable cost.
You can’t optimize what you can’t see. ParallelIQ gives you complete visibility into your AI workloads, model behavior, and GPU efficiency — so you can scale AI with discipline, reliability, and predictable cost.
Your GPU bill doubled. Your throughput didn’t.
Your GPU bill doubled. Your throughput didn’t.
Your GPU bill doubled. Your throughput didn’t.
Your GPU bill doubled. Your throughput didn’t.
Why AI Infra Is Breaking?
AI infrastructure today wasn’t designed for the way modern models behave.
ParallelIQ addresses the three structural failures at the core of AI operations
Zero Visibility Into AI Workloads
Most teams don’t know which models are running, which versions are deployed, or how pipelines depend on each other.
Most teams don’t know which models are running, which versions are deployed, or how pipelines depend on each other.
Most teams don’t know which models are running, which versions are deployed, or how pipelines depend on each other.
Impact: Unknown risk, unpredictable cost, and operational blind spots.
Impact: Unknown risk, unpredictable cost, and operational blind spots.
Impact: unknown risk, unpredictable cost, and operational blind spots.
Our solution: Delivers a complete model inventory, dependency mapping, and clear workload insights.
PIQC Benefit: full model inventory, dependency mapping, and workload insights.
PIQC Benefit: full model inventory, dependency mapping, and workload insights.
GPUs Are Busy — But Not Productive
Inefficient batching, incorrect concurrency, poor autoscaling, and suboptimal instance selection keep GPUs active without delivering proportional throughput.
Inefficient batching, incorrect concurrency, poor autoscaling, and suboptimal instance selection keep GPUs active without delivering proportional throughput.
Inefficient batching, incorrect concurrency, poor autoscaling, and suboptimal instance selection keep GPUs active without delivering proportional throughput.
Impact: GPU bill grows faster than throughput.
Impact: GPU bill grows faster than throughput.
Impact: GPU bill grows faster than throughput.
Our solution: Provides model-aware profiling, efficiency signals, and clear guidance to eliminate waste.
PIQC Benefit: profiling, efficiency warnings, and model-aware optimization.
PIQC Benefit: profiling, efficiency warnings, and model-aware optimization.
No Clarity on What's Actually Running
Schedulers, autoscaling, and observability were designed for stateless web traffic — not memory- heavy ML inference pipelines with strict latency requirements.
Schedulers, autoscaling, and observability were designed for stateless web traffic — not memory- heavy ML inference pipelines with strict latency requirements.
Schedulers, autoscaling, and observability were designed for stateless web traffic — not memory- heavy ML inference pipelines with strict latency requirements.
Impact: Unstable scaling, unpredictable latency, and operational chaos.
Impact: Unstable scaling, unpredictable latency, and operational chaos.
Impact: poor scaling, unpredictable latency, and operational chaos.
Our solution: Provides Al-aware runtime signals, compliance checks, and dependency-driven orchestration readiness.
Our solution: Provides Al-aware runtime signals, compliance checks, and dependency-driven orchestration readiness.
PIQC Benefit: AI-aware insights, compliance checks, and dependency-driven orchestration readiness.
Meet ParallelIQ
The AI-Runtime Intelligence Layer
Meet ParallelIQ The AI-Runtime Intelligence Layer
ParallelIQ gives ML and platform teams the visibility, context, and intelligence needed to run AI workloads efficiently at scale.
Model & Workload Discovery
Automatically identify every model, GPU, batch size, and runtime configuration across your cluster.
Model & Workload Discovery
Automatically identify every model, GPU, batch size, and runtime configuration across your cluster.
Model & Workload Discovery
Automatically identify every model, GPU, batch size, and runtime configuration across your cluster.
Real GPU Cost & Efficiency Mapping
Link throughput, memory behavior, and scaling patterns directly to GPU spend and efficiency.
Real GPU Cost & Efficiency Mapping
Link throughput, memory behavior, and scaling patterns directly to GPU spend and efficiency.
Real GPU Cost & Efficiency Mapping
Link throughput, memory behavior, and scaling patterns directly to GPU spend and efficiency.
Predictive Orchestration
Move beyond reactive autoscaling with orchestration that anticipates GPU demand before spikes occur.
Predictive Orchestration
Move beyond reactive autoscaling with orchestration that anticipates GPU demand before spikes occur.
Predictive Orchestration
Move beyond reactive autoscaling with orchestration that anticipates GPU demand before spikes occur.
Declarative Model Metadata (ModelSpec)
Give infrastructure a machine-readable description of model attributes, dependencies, and operational requirements.
Declarative Model Metadata (ModelSpec)
Give infrastructure a machine-readable description of model attributes, dependencies, and operational requirements.
Declarative Model Metadata (ModelSpec)
Give infrastructure a machine-readable description of model attributes, dependencies, and operational requirements.
Expert Services to Optimize Your AI Infrastructure
ParallelIQ pairs deep AI-runtime expertise with hands-on execution to help teams reduce waste, improve reliability, and scale with confidence.
1–2 weeks
Infrastructure Audit
A comprehensive assessment of your GPU fleet, AI workloads, spend, and deployment architecture.
GPU utilization and efficiency report
Cost allocation and spend attribution
Latency and throughput profiling
Compliance and best-practice gaps
2–4 weeks
Optimization Sprint
Hands-on engineering to eliminate inefficiencies and harden your AI infrastructure.
Ongoing
Managed Optimization
Continuous oversight as models, workloads, and costs evolve.
1–2 weeks
Infrastructure Audit
A comprehensive assessment of your GPU fleet, AI workloads, spend, and deployment architecture.
GPU utilization and efficiency report
Cost allocation and spend attribution
Latency and throughput profiling
Compliance and best-practice gaps
2–4 weeks
Optimization Sprint
Hands-on engineering to eliminate inefficiencies and harden your AI infrastructure.
Ongoing
Managed Optimization
Continuous oversight as models, workloads, and costs evolve.
1–2 weeks
Infrastructure Audit
A comprehensive assessment of your GPU fleet, AI workloads, spend, and deployment architecture.
GPU utilization and efficiency report
Cost allocation and spend attribution
Latency and throughput profiling
Compliance and best-practice gaps
2–4 weeks
Optimization Sprint
Hands-on engineering to eliminate inefficiencies and harden your AI infrastructure.
Ongoing
Managed Optimization
Continuous oversight as models, workloads, and costs evolve.
Products
Core building blocks that bring visibility, control, and efficiency to AI infrastructure.
Open-Core
Open-Core
Open-Core
PIQC Introspect
Your cluster’s X-ray.
Complete model inventory
Complete model inventory
Complete model inventory
GPU and accelerator characteristics
GPU and accelerator characteristics
GPU and accelerator characteristics
AI workload detection
AI workload detection
AI workload detection
Static and runtime configuration insights
Static and runtime configuration insights
Static and runtime configuration insights
Private Beta
Private Beta
Private Beta
Predictive Orchestration
Orchestration built for ML — not microservices.
Predicts GPU demand ahead of spikes
Predicts GPU demand ahead of spikes
Predicts GPU demand ahead of spikes
Reduces cold-start and warm-up latency
Reduces cold-start and warm-up latency
Reduces cold-start and warm-up latency
Manages hot, warm, and cold GPU pools
Manages hot, warm, and cold GPU pools
Manages hot, warm, and cold GPU pools
Plans capacity with cost awareness
Plans capacity with cost awareness
Plans capacity with cost awareness
Open Schema
Open Schema
Open Schema
ModelSpec
A machine-readable contract for running models in production.
Model dependencies and pipeline context
Model dependencies and pipeline context
Model dependencies and pipeline context
GPU, memory, and runtime requirements
GPU, memory, and runtime requirements
GPU, memory, and runtime requirements
Latency and throughput SLOs
Latency and throughput SLOs
Latency and throughput SLOs
Security, compliance, and guardrails
Security, compliance, and guardrails
Security, compliance, and guardrails
Partners & Ecosystem
Partners & Ecosystem
Call out the partner types
Call out the partner types
GPU Cloud Providers
Scalable, high-performance GPU infrastructure for AI training and inference, available globally across cloud and on-prem environments.
Role: compute capacity, elasticity, hardware innovation
Role: trust, auditability, and risk management
AI monitoring, security, and compliance vendors for regulated production.
Role: trust, auditability, and risk management
ML Platforms
End-to-end ML platforms for streamlined development, deployment, and management at enterprise scale.
Role: developer productivity and ML workflows
Universities & HPC Centers
Leading research institutions advancing AI systems, algorithms, and high-performance computing through cutting-edge research.
Role: innovation, validation, and next-generation architectures
GPU Cloud Providers
Scalable, high-performance GPU infrastructure for AI training and inference, available globally across cloud and on-prem environments.
Role: compute capacity, elasticity, hardware innovation
Role: trust, auditability, and risk management
AI monitoring, security, and compliance vendors for regulated production.
Role: trust, auditability, and risk management
ML Platforms
End-to-end ML platforms for streamlined development, deployment, and management at enterprise scale.
Role: developer productivity and ML workflows
Universities & HPC Centers
Leading research institutions advancing AI systems, algorithms, and high-performance computing through cutting-edge research.
Role: innovation, validation, and next-generation architectures
GPU Cloud Providers
Scalable, high-performance GPU infrastructure for AI training and inference, available globally across cloud and on-prem environments.
Role: compute capacity, elasticity, hardware innovation
Role: trust, auditability, and risk management
AI monitoring, security, and compliance vendors for regulated production.
Role: trust, auditability, and risk management
ML Platforms
End-to-end ML platforms for streamlined development, deployment, and management at enterprise scale.
Role: developer productivity and ML workflows
Universities & HPC Centers
Leading research institutions advancing AI systems, algorithms, and high-performance computing through cutting-edge research.
Role: innovation, validation, and next-generation architectures
Case Studies
Real results: lower costs, faster launches, longer runway.
Training
Cutting AI Training Costs by 40% — No Trade-Offs in Performance
Training
Cutting AI Training Costs by 40% — No Trade-Offs in Performance
Training
Faster AI Model Releases with 40% Fewer Incidents
Training
Faster AI Model Releases with 40% Fewer Incidents
Training
Cutting Drift Detection by 85%: Observability that Transforms MLOps
Training
Cutting Drift Detection by 85%: Observability that Transforms MLOps
Training
Compliance-Aware AI Data Infrastructure for Healthcare
Training
Compliance-Aware AI Data Infrastructure for Healthcare
The AI Infrastructure Journal
Deep dives into architecture, performance tuning, and operational excellence.
AI/ML Model Operations
The Financial Fault Line Beneath GPU Clouds
AI/ML Model Operations
The Financial Fault Line Beneath GPU Clouds
AI/ML Model Operations
Variability Is the Real Bottleneck in AI Infrastructure
AI/ML Model Operations
Variability Is the Real Bottleneck in AI Infrastructure
AI/ML Model Operations
Orchestration, Serving, and Execution: The Three Layers of Model Deployment
AI/ML Model Operations
Orchestration, Serving, and Execution: The Three Layers of Model Deployment
AI/ML Model Operations
The Financial Fault Line Beneath GPU Clouds
AI/ML Model Operations
Variability Is the Real Bottleneck in AI Infrastructure
AI/ML Model Operations
Orchestration, Serving, and Execution: The Three Layers of Model Deployment
AI/ML Model Operations
The Checklist Manifesto, Revisited for AI Infrastructure
Don’t let performance bottlenecks slow you down. Optimize your stack and accelerate your AI outcomes.
Don’t let performance bottlenecks slow you down. Optimize your stack and accelerate your AI outcomes.
Don’t let performance bottlenecks slow you down. Optimize your stack and accelerate your AI outcomes.
Don’t let performance bottlenecks slow you down. Optimize your stack and accelerate your AI outcomes.
Services
© 2025 ParallelIQ. All rights reserved.
Services
© 2025 ParallelIQ. All rights reserved.
Services
© 2025 ParallelIQ. All rights reserved.