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Inferonomics

Use cases

Inference at scale, training jobs, and hybrid enterprise—with verified stack blueprints.

Core use cases

Inference at scale

ML platform and product teams who need to serve models in production with predictable latency and cost.

Benefits

  • Managed inference endpoints with health checks and rolling updates
  • Autoscaling (including scale-to-zero) to minimize idle cost
  • Low idle cost: pay only when traffic is served
  • Multiple backends and verified stack blueprints (vLLM, Triton, custom)

Related features

Inference endpoints
Autoscaling
Verified blueprints
Cost caps

Training jobs

Data science and ML teams running distributed training with the need for reliability and resource fairness.

Benefits

  • Checkpoint durability: persist to object storage and resume from last state
  • Quotas and fair-share scheduling so teams get predictable capacity
  • Preemption-aware scheduling (roadmap): use spot/preemptible with automatic resume
  • Single control plane for both inference and training workloads

Related features

Training jobs
Checkpoints
Quotas
Preemption (roadmap)

Hybrid enterprise

Enterprises that need data locality, region/zone controls, and on-prem-first with cloud burst.

Benefits

  • Data locality: place workloads where your data lives (on-prem or specific region)
  • Region and zone controls via placement policies
  • On-prem first: use your GPU clusters before bursting to cloud
  • Single pane of glass across all environments

Related features

Hybrid placement
On-prem first
Placement policies
Multi-cloud

Blueprint spotlight

Verified stacks you can deploy in minutes. Pre-validated hardware targets and runtime versions.

vLLM inference

High-throughput LLM serving with PagedAttention and continuous batching.

Hardware target

NVIDIA GPU (A100, H100, L4, T4)

Runtime versions

  • vLLM 0.4.x
  • CUDA 12.x
  • Python 3.10+
Deploy in minutes

Triton Inference Server

Multi-framework inference (TensorRT, ONNX, PyTorch) with dynamic batching.

Hardware target

NVIDIA GPU (Ampere or newer)

Runtime versions

  • Triton 2.40+
  • CUDA 12.x
  • cuDNN 8.x
Deploy in minutes

ComfyUI

Stable Diffusion and image generation workflows with node-based UI.

Hardware target

NVIDIA GPU (8GB+ VRAM)

Runtime versions

  • ComfyUI latest
  • PyTorch 2.x
  • CUDA 12.x
Deploy in minutes

Whisper transcription

OpenAI Whisper for speech-to-text at scale with batch and streaming.

Hardware target

NVIDIA GPU (T4, L4, A10)

Runtime versions

  • Whisper (large-v3)
  • faster-whisper / CTranslate2
  • Python 3.10+
Deploy in minutes

Find your use case

Tell us about your workloads and we will show you how InferoFabric fits.