High-Performance LLM Serving.
Unified API.
Kubernetes-native distributed inference delivering low-latency serving of open-source LLMs through one API.
Trusted by teams running AI at production scale
The Challenges of LLM Inference
Running LLMs at scale requires solving hard infrastructure problems that distract from building your product.
Self-Hosting is Hard
Self-hosting LLMs requires complex infrastructure, constant maintenance, and deep expertise that most teams lack.
GPU Underutilization
GPUs sit idle while costs accumulate. Without intelligent scheduling, resource efficiency plummets.
Scaling is Complex
Distributed inference across multiple nodes and GPUs requires custom orchestration that's hard to build and maintain.
Latency Spikes
Traffic bursts cause unpredictable latency. Without load balancing, user experience suffers under load.
How wheels.ai Works
A distributed inference platform that handles the complexity so you don't have to.
Your App
Single SDK integration
wheels.ai
Distributed inference layer
Open-Source LLMs
Deployed to your GPUs
Distributed Inference
Execute requests across multiple GPUs and nodes with automatic workload distribution.
Kubernetes-Native
Purpose-built for Kubernetes with native orchestration and resource management.
Dynamic Scaling
Automatically scale inference capacity based on load. Zero manual intervention.
Optimized Performance
Intelligent scheduling and load balancing for low-latency, high-throughput serving.
Built for Production Inference
Infrastructure-grade capabilities for teams running LLMs at scale.
Kubernetes-Native Architecture
Built from the ground up for Kubernetes. Native pod orchestration, resource management, and scaling.
Distributed Multi-GPU Inference
Serve large models across multiple GPUs and nodes. Automatic tensor parallelism and model sharding.
Unified API for Open-Source Models
One API to access leading open-source LLMs. No code changes when adding or upgrading models.
Dynamic Load Balancing
Intelligent request distribution across all available inference endpoints for optimal throughput.
Horizontal Auto-Scaling
Scale out automatically based on demand. Scale in when idle to optimize costs.
Low-Latency Serving
Optimized inference pipelines for minimal latency. Sub-second response times at scale.
Efficient GPU Utilization
Pack workloads intelligently to maximize GPU usage and minimize wasted resources.
Production Observability
Real-time metrics, distributed tracing, and alerts for complete visibility into inference performance.
Serve Open-Source LLMs at Scale
Access production-ready models. Add or upgrade without API changes.
Simple Inference API
Same API across all models and deployments. Scale without code changes.
import openaiclient = openai.OpenAI( api_key="WHEELS_API_KEY", base_url="https://api.wheels.ai/v1")response = client.chat.completions.create( model="Llama-3.3-70B-Instruct", messages=[{"role": "user", "content": "What is machine learning?"}])print(response.choices[0].message.content)Scale your deployment without changing a single line of code. The same API works whether you're running one GPU or hundreds.
Why wheels.ai
Infrastructure you trust with real workloads.
Built for Production
Enterprise-grade reliability. Designed for real workloads, not demos.
Designed for Scale
Purpose-built for distributed systems. Run across multiple nodes and GPUs.
Optimized Performance
Low-latency inference pipelines with intelligent scheduling and load balancing.
Ready to Ship Smarter AI Apps?
Join the teams that trust wheels.ai for their production LLM infrastructure. Start building in minutes.
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