Your GPUs. Production inference.
Deploy InferX inside your own infrastructure — Kubernetes, bare metal, private cloud, or GPU cloud. Fast cold starts, scale-to-zero, isolated workloads, and OpenAI-compatible APIs. Reduce inference infrastructure costs by 30–70%*.
*Savings depend on workload patterns, model size, traffic shape, and existing GPU utilization.
from openai import OpenAI
# the standard client, pointed at your own deployment
client = OpenAI(
base_url="http://inferx.internal/v1",
api_key="YOUR_INFERX_KEY",
)
stream = client.chat.completions.create(
model="gpt-oss-20b",
messages=[{"role": "user", "content": "Summarize this ticket."}],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")- Kubernetes
- EKS
- GKE
- AKS
- OpenShift
- On-prem
- Bare metal
- Private cloud
- GPU cloud
Owning GPUs is not the same as operating inference.
Provisioned capacity sits idle between workloads, cold paths add latency before traffic is served, and every new model means another serving stack to operate. The hardware is not the bottleneck — the runtime is.
Average GPU utilization. Models hold memory even when they aren’t serving.
Up to 80% GPU utilization. Capacity follows demand instead of sitting reserved.
Idle GPU capacity
Provisioned capacity waits between workloads.
Slow model activation
Cold paths add latency before traffic is served.
Low utilization
Useful work is separated from available hardware.
Fragmented serving stacks
Teams operate separate serving paths per model.
Security and isolation
Controls get harder as deployments spread.
boundary
You bring the GPUs. InferX does the rest.
Nothing moves. InferX installs inside your existing environment and operates the inference layer on top of the hardware and policies you already run.
- —GPU infrastructure
- —Networking
- —Storage
- —Your models
- —Your environment
- —Infrastructure policies
- +Inference platform
- +Secure container runtime
- +Model serving
- +Scheduling & orchestration
- +Scaling & recovery
- +Production APIs (OpenAI-compatible)
Your infrastructure. Your models. Your data. InferX turns it into a production inference service.
Platform
High-performance inference with built-in security.
A complete inference layer, deployed on your GPUs — from the API your applications call, down to the isolation boundary beneath each workload.
How InferX fits into your environment.
A request arrives at an OpenAI-compatible endpoint inside your boundary. InferX restores model-ready state, attaches GPU capacity, serves the response, and releases the GPU when the workload goes quiet.
Application requests
A service running inside your environment calls one of your endpoints.
One node or 1,000.
The same inference platform operates across a single GPU server or an entire estate. Start with one, expand without migrating.
Bare metal · Kubernetes · Private cloud · GPU cloud
Production inference without the limits.
Sub-second cold starts
A snapshot-based runtime restores model-ready state instead of reloading weights from scratch.
Multi-GPU at scale
Tensor parallelism across 2, 4, and 8 GPUs, plus data parallelism with NCCL.
Higher density
Share GPUs across multiple models and workloads instead of reserving capacity per model.
Secure by design
An isolated container runtime with GPU and NCCL-level protection beneath every workload.
Run anywhere
On-prem, private cloud, or public cloud — the same platform across every environment.
OpenAI-compatible
Integrate with your existing tools, agents, and workflows without rewriting clients.
Deploy where your infrastructure already lives.
InferX runs inside the environment you already operate. No migration, no separate control plane, no new cloud.
Kubernetes
EKS, GKE, AKS, OpenShift, and on-prem Kubernetes distributions.
Bare metal GPU servers
Dedicated GPU servers without moving existing workloads.
Private cloud
Keep model traffic and operations inside your own network.
On-prem enterprise environments
Deployment planning for regulated environments.
GPU cloud providers
An inference layer across rented GPU capacity.
Restricted environments
Reviewed during discovery where applicable.
data control
Your models, traffic, and infrastructure stay under your control.
A private platform install runs entirely inside your boundary. Nothing is routed to an InferX-controlled cloud.
Built for production inference workloads.
Fast cold starts from restored runtime state
Scale-to-zero when idle
OpenAI-compatible APIs
Production endpoint experience from hosted InferX runtime
Real enterprise/private deployment conversations
playbook
How deployment works.
Deployment starts on a limited GPU pool and expands across the boundary once the runtime has been validated against your own workloads.
Discovery
Map workloads, models, GPUs, access, and security requirements.
Pilot
Install InferX on a limited GPU pool, alongside your existing serving paths.
Validation
Measure cold starts, latency, utilization, and API compatibility on your own workloads.
Production rollout
Expand across the approved deployment boundary.
Ongoing support
Support, monitoring, upgrades, and capacity planning.
Platform pricing that matches your infrastructure.
InferX platform pricing is structured around a platform license, a deployment and support package, and cluster size or GPU footprint — so it tracks the infrastructure you actually run.
asked
Turn your GPU capacity into a production inference platform.
Tell us what you’re running today — clusters, GPU types, and the models you need in production. We’ll map the deployment.
Talk to the Platform Team →