InferenceIQ
Inference intelligence before you deploy
Engine picks, GPU fit, quantization, cost, and scaling guidance across the stacks you already use — so every recommendation matches your model and your priorities.
The deployment guesswork problem
Most teams deploy models by intuition. InferenceIQ replaces guesswork with data-backed recommendations.
Before InferenceIQ
- Manual GPU guessing based on vibes, not data
- Trial-and-error engine selection across 6+ options
- Surprise cloud bills after deployment
- No quantization guidance for your specific model
- One-size-fits-all configs that waste resources
After InferenceIQ
- AI-scored GPU picks matched to your model architecture
- Engine recommendation based on model type and size
- Cost projected across 19 cloud configs before deploy
- Quantization strategy tailored per model and quality needs
- Deployment-ready export: K8s manifests, Helm, or API payload
Five dimensions. One recommendation.
Every recommendation is scored across five dimensions so you see the trade-offs visually and choose the configuration that matches your priorities.
Latency
End-to-end response time scoring
Throughput
Tokens per second capacity
Cost
Cost-per-inference optimization
Reliability
Uptime and fault tolerance
Sustainability
Energy and carbon efficiency
Three steps to optimized inference
Point at your model
Paste a HuggingFace URL or select from your registry. InferenceIQ auto-extracts architecture, params, and context length.
Get recommendations
Scored across 5 dimensions with confidence ratings, plain-English explanations, and alternative options.
Deploy in one click
Export as Kubernetes manifests, Helm values, or API payloads. Production-ready. No manual translation.
13+ GPU profiles. Every major vendor.
NVIDIA, AMD, and Intel — from edge T4s to Blackwell B200s. InferenceIQ scores them all.
Every major inference engine
High-throughput serving for 7B-405B parameter models
HuggingFace-native text generation inference
Maximum performance on NVIDIA hardware
Hardware-agnostic NLP and vision models
Multi-framework model serving at scale
Quantized inference on consumer hardware
cloud configurations compared in seconds
AWS · GCP · Azure · Lambda Labs · CoreWeave · RunPod · Together AI
Enterprise-grade by design
Built for teams that need explainability, auditability, and production readiness from day one.
Confidence Scores & Explanations
Every recommendation includes a confidence score and plain-English explanation of why it was chosen, what trade-offs were made, and what alternatives were considered.
5 LLM Backends
OpenAI, Anthropic, Kimi/Moonshot, or self-hosted Ollama as reasoning engine. Falls back to deterministic rules when no LLM is available.
HuggingFace Deep Integration
Point at any HF model URL. Auto-extracts architecture, parameter count, quantization compatibility, attention type, and context length.
Scaling Policy Generation
HPA config with min/max replicas, autoscaling thresholds, scale-up and scale-down behavior optimized for your workload pattern.
Deployment-Ready Export
Kubernetes manifests, Helm values, or direct API payload. One click from recommendation to deployment. No manual translation.
Deterministic Fallback
Rule-based engine that works without any LLM backend. Every recommendation is reproducible and auditable — no black-box decisions.
Ready to optimize?
Deploy models that perform, not just models that run
Get data-backed recommendations for every model deployment. Engine picks, GPU fit, quantization, cost, and scaling — in seconds, not days.