EdgeFlow

Now in private beta

Deploy LLM/VLM anywhere.
Run efficiently everywhere.

The compatibility layer for AI inference. Deploy LLMs and VLMs
across CPU, GPU, and edge—without rewriting a single line.

View Documentation

Open source · No GPU required · Deploy in minutes

Trusted by engineering teams at

Built for production AI

Everything you need to ship models without infrastructure headaches.

Unified Control

One console for every deployment

Track CPU and GPU workloads, manage access policies, and promote releases from staging to production without leaving the dashboard.

Ops Automation

Close the loop on reliability

Streaming metrics, proactive SLO alerts, and guided runbooks help your operators resolve incidents before customers notice.

Enterprise Ready

Security & compliance baked in

Audit trails, role-based controls, and SOC2-ready policies make it easy to bring EdgeFlow into regulated environments.

InferX

Core Inference Engine

Executes LLMs and VLMs efficiently across CPU, GPU, and edge hardware. Ensures optimal performance independent of environment.

ModelRun

Deployment & Management

Handles model packaging, integration, and rollout. Automates deployment pipelines for enterprises.

CoreShift

Dynamic Hardware Optimizer

Monitors and reallocates compute resources in real time. Cuts inference cost by 40% through efficient utilization.

Unified Stack

End-to-End Platform

Model Abstraction Layer, Compiler Optimization, Unified Runtime, and Monitoring & CI/CD Integration.

The EdgeFlow Stack

Three layers. One unified platform.

Inference Engine

InferX — Universal Runtime Layer

Executes LLMs and VLMs efficiently across any hardware. Quantization and optimized CPU kernels deliver strong throughput without GPU dependency. Add acceleration when you need it.

7.7× faster with context caching (3.1s vs 24s baseline)

inferx_example.py

from edgeflow import InferX

# Load model with CPU optimization
model = InferX.load("qwen-vl-3b", device="cpu")

# Generate with streaming
response = model.generate(
    prompt="Analyze this chart",
    image=image_data,
    max_tokens=512,
    stream=True
)

for chunk in response:
    print(chunk, end="")

CPU Pool

GPU Pool

Edge Nodes

CoreShift Optimizer

40%

Cost Saved

24/7

Monitoring

<50ms

Rebalance

Resource Optimizer

CoreShift — Dynamic Hardware Balancer

Monitors and reallocates compute resources in real time. Balances load across CPUs, GPUs, and edge nodes automatically. Cuts inference cost by 40% through efficient utilization.

40% infrastructure cost reduction through smart allocation

Deployment & Management

ModelRun — Ship Models, Not YAML

Package. Deploy. Rollback. Automated pipelines for ML teams. Staging-to-production workflows that adapt to your infrastructure.

Package your model into an EdgeFlow bundle.

→

Expose REST and streaming endpoints.

→

Ship to edge or cloud with your CI pipeline.

→

Track latency and cost with built-in metrics.

Why engineering teams choose EdgeFlow

Keep Data On-Prem

Deploy at the edge or in your own data center. Full control over sensitive workloads. Air-gapped options available.

One Runtime, Everywhere

LLM and VLM on the same interface. Laptop, cloud, or edge—no code changes. Same APIs, same behavior.

Quantized Performance

Optimized kernels and quantization deliver GPU-class throughput on CPU infrastructure. No expensive hardware required.

request.json

{
  "model": "edgeflow-qwen-3-vl",
  "input": {
    "prompt": "Summarize this image",
    "image": "data:image/png;base64,..."
  },
  "params": {
    "max_tokens": 2048,
    "temperature": 0.2
  }
}

terminal

curl -X POST \
  https://api.edgeflow.local/v1/generate \
  -H "Authorization: Bearer <token>" \
  -d @request.json

Key Features

Everything you need to deploy AI models efficiently at scale.

CPU-first inference

Run LLM and VLM on any hardware with low memory footprint and strong throughput. Our optimized kernels and quantization techniques deliver production-grade performance without requiring expensive GPUs.

Learn more

Consistent deploys

One runtime across laptop, data center, and edge. Same APIs, same behavior.

Lower TCO

Reduce GPU spend using CPU pools without loss of quality for many workloads.

Enterprise ready

FastAPI endpoints, auth, observability, and CI/CD hooks built in. Deploy with confidence using battle-tested infrastructure.

Simple pricing. Scale as you grow.

Start free, upgrade when you need more power.

Starter

Forever free

Local development
Single model
Community support

Popular

Team

$499

/month, billed monthly

CPU clusters
REST and streaming
Metrics and dashboards
Email support

Enterprise

Custom

For your scale

SLA and SSO
Air-gapped deploy
On-site support
Custom integrations

Frequently Asked Questions

Find answers to common questions about EdgeFlow

Do I need GPUs?

No. EdgeFlow targets CPUs first. You add GPUs later if needed for additional performance.

Which models are supported?

LLM and VLM families with quantization support: Qwen-VL, Gemma, Llama, and more.

Can I deploy on-prem?

Yes. Air-gapped options available for enterprise customers.

What's the typical TCO reduction?

CoreShift reduces infrastructure costs by 40% through efficient CPU utilization.

Is EdgeFlow open source?

Yes. Core components are MIT-licensed. Commercial and usage-based options also available.

Ready to deploy AI anywhere?

Join teams running production inference without GPU lock-in.

Talk to an Engineer

Deploy LLM/VLM anywhere. Run efficiently everywhere.